{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save and Load Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Saving and loading trained Deep Learning models has multiple valuable uses. These models are often costly to train; storing a pre-trained model can help reduce costs as it can be loaded and reused to forecast multiple times. Moreover, it enables Transfer learning capabilities, consisting of pre-training a flexible model on a large dataset and using it later on other data with little to no training. It is one of the most outstanding 🚀 achievements in Machine Learning 🧠 and has many practical applications.\n",
    "\n",
    "In this notebook we show an example on how to save and load `NeuralForecast` models.\n",
    "\n",
    "The two methods to consider are:<br>\n",
    "1. `NeuralForecast.save`: Saves models into disk, allows save dataset and config.<br>\n",
    "2. `NeuralForecast.load`: Loads models from a given path.<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{.callout-important}\n",
    "This Guide assumes basic knowledge on the NeuralForecast library. For a minimal example visit the [Getting Started](../getting-started/02_quickstart.ipynb) guide.\n",
    ":::"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can run these experiments using GPU with Google Colab.\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/Nixtla/neuralforecast/blob/main/nbs/examples/Save_Load_models.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Installing NeuralForecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "!pip install neuralforecast"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Loading AirPassengers Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this example we will use the classical [AirPassenger Data set](https://www.kaggle.com/datasets/rakannimer/air-passengers). Import the pre-processed AirPassenger from `utils`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from neuralforecast.utils import AirPassengersDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1949-01-31</td>\n",
       "      <td>112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1949-02-28</td>\n",
       "      <td>118.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1949-03-31</td>\n",
       "      <td>132.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1949-04-30</td>\n",
       "      <td>129.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1949-05-31</td>\n",
       "      <td>121.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   unique_id         ds      y\n",
       "0        1.0 1949-01-31  112.0\n",
       "1        1.0 1949-02-28  118.0\n",
       "2        1.0 1949-03-31  132.0\n",
       "3        1.0 1949-04-30  129.0\n",
       "4        1.0 1949-05-31  121.0"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_df = AirPassengersDF\n",
    "Y_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Model Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we instantiate and train three models: `NBEATS`, `NHITS`, and `AutoMLP`. The models with their hyperparameters are defined in the `models` list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "\n",
    "from ray import tune\n",
    "\n",
    "from neuralforecast.core import NeuralForecast\n",
    "from neuralforecast.auto import AutoMLP\n",
    "from neuralforecast.models import NBEATS, NHITS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "logging.getLogger('pytorch_lightning').setLevel(logging.ERROR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Seed set to 1\n",
      "Seed set to 1\n"
     ]
    }
   ],
   "source": [
    "horizon = 12\n",
    "models = [NBEATS(input_size=2 * horizon, h=horizon, max_steps=50),\n",
    "          NHITS(input_size=2 * horizon, h=horizon, max_steps=50),\n",
    "          AutoMLP(# Ray tune explore config\n",
    "                  config=dict(max_steps=100, # Operates with steps not epochs\n",
    "                              input_size=tune.choice([3*horizon]),\n",
    "                              learning_rate=tune.choice([1e-3])),\n",
    "                  h=horizon,\n",
    "                  num_samples=1, cpus=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "nf = NeuralForecast(models=models, freq='ME')\n",
    "nf.fit(df=Y_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Produce the forecasts with the `predict` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c01118bf2b724117843a84a560ffe33b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: |                                                                                                 …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ca820e5809ad4b0884ebb342bb4c779a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: |                                                                                                 …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ea9d52ab84964086b2eb108531ac1514",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: |                                                                                                 …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>NBEATS</th>\n",
       "      <th>NHITS</th>\n",
       "      <th>AutoMLP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1961-01-31</td>\n",
       "      <td>446.882172</td>\n",
       "      <td>447.219238</td>\n",
       "      <td>454.914154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1961-02-28</td>\n",
       "      <td>465.145813</td>\n",
       "      <td>464.558014</td>\n",
       "      <td>430.188446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1961-03-31</td>\n",
       "      <td>469.978424</td>\n",
       "      <td>474.637238</td>\n",
       "      <td>458.478577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1961-04-30</td>\n",
       "      <td>493.650665</td>\n",
       "      <td>502.670349</td>\n",
       "      <td>477.244507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1961-05-31</td>\n",
       "      <td>537.569275</td>\n",
       "      <td>559.405212</td>\n",
       "      <td>522.252991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   unique_id         ds      NBEATS       NHITS     AutoMLP\n",
       "0        1.0 1961-01-31  446.882172  447.219238  454.914154\n",
       "1        1.0 1961-02-28  465.145813  464.558014  430.188446\n",
       "2        1.0 1961-03-31  469.978424  474.637238  458.478577\n",
       "3        1.0 1961-04-30  493.650665  502.670349  477.244507\n",
       "4        1.0 1961-05-31  537.569275  559.405212  522.252991"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_hat_df = nf.predict()\n",
    "Y_hat_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We plot the forecasts for each model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utilsforecast.plotting import plot_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x350 with 1 Axes>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_series(Y_df, Y_hat_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Save models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To save all the trained models use the `save` method. This method will save both the hyperparameters and the learnable weights (parameters).\n",
    "\n",
    "The `save` method has the following inputs:\n",
    "\n",
    "* `path`: directory where models will be saved.\n",
    "* `model_index`: optional list to specify which models to save. For example, to only save the `NHITS` model use `model_index=[2]`.\n",
    "* `overwrite`: boolean to overwrite existing files in `path`. When True, the method will only overwrite models with conflicting names.\n",
    "* `save_dataset`: boolean to save `Dataset` object with the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nf.save(path='./checkpoints/test_run/',\n",
    "        model_index=None, \n",
    "        overwrite=True,\n",
    "        save_dataset=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each model, two files are created and stored:\n",
    "\n",
    "* `[model_name]_[suffix].ckpt`: Pytorch Lightning checkpoint file with the model parameters and hyperparameters.\n",
    "* `[model_name]_[suffix].pkl`: Dictionary with configuration attributes.\n",
    "\n",
    "Where `model_name` corresponds to the name of the model in lowercase (eg. `nhits`). We use a numerical suffix to distinguish multiple models of each class. In this example the names will be `automlp_0`, `nbeats_0`, and `nhits_0`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{.callout-important}\n",
    "The `Auto` models will be stored as their base model. For example, the `AutoMLP` trained above is stored as an `MLP` model, with the best hyparparameters found during tuning.\n",
    ":::"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Load models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the saved models with the `load` method, specifying the `path`, and use the new `nf2` object to produce forecasts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Seed set to 1\n",
      "Seed set to 1\n",
      "Seed set to 1\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "43e8f7fe74e747109448782f9c8ada58",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: |                                                                                                 …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c63299cb40cd4375a684edc9dc050436",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: |                                                                                                 …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "65dda721c0f14c36b33a157fa22da7b3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: |                                                                                                 …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>NHITS</th>\n",
       "      <th>NBEATS</th>\n",
       "      <th>AutoMLP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1961-01-31</td>\n",
       "      <td>447.219238</td>\n",
       "      <td>446.882172</td>\n",
       "      <td>454.914154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1961-02-28</td>\n",
       "      <td>464.558014</td>\n",
       "      <td>465.145813</td>\n",
       "      <td>430.188446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1961-03-31</td>\n",
       "      <td>474.637238</td>\n",
       "      <td>469.978424</td>\n",
       "      <td>458.478577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1961-04-30</td>\n",
       "      <td>502.670349</td>\n",
       "      <td>493.650665</td>\n",
       "      <td>477.244507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1961-05-31</td>\n",
       "      <td>559.405212</td>\n",
       "      <td>537.569275</td>\n",
       "      <td>522.252991</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "   unique_id         ds       NHITS      NBEATS     AutoMLP\n",
       "0        1.0 1961-01-31  447.219238  446.882172  454.914154\n",
       "1        1.0 1961-02-28  464.558014  465.145813  430.188446\n",
       "2        1.0 1961-03-31  474.637238  469.978424  458.478577\n",
       "3        1.0 1961-04-30  502.670349  493.650665  477.244507\n",
       "4        1.0 1961-05-31  559.405212  537.569275  522.252991"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nf2 = NeuralForecast.load(path='./checkpoints/test_run/')\n",
    "Y_hat_df2 = nf2.predict()\n",
    "Y_hat_df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide\n",
    "pd.testing.assert_frame_equal(Y_hat_df, Y_hat_df2[Y_hat_df.columns])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, plot the forecasts to confirm they are identical to the original forecasts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Hi45+3iT4edPRz5sOft6EetSL2+siDnHo0CFXh9Ag1Iv0+rRp0wgPD2fs2LGVx7p37467uzuLFi2qPLZ7924SExPp06cPAH369GHr1q2kpaVVjlmwYAEBAQEkJCTU3RMQERERERERERERkfPC4owcANr6ehHibsFqwK78Ir5JzWTy/qP8ZfN+Ovy+jW4rtvNFygkXRysidcnlqXSbzca0adO48cYbcXP7I5zAwEBuueUWHnzwQUJCQggICODee++lT58+9O7dG4ARI0aQkJDA9ddfz8svv8yxY8d44oknuPvuu1WJJiIiIiIiIiIiIiI1klFaxtbcQgC+7NKSCA83UopL2ZZXyPa8wsr/PVRYQkpxKW8eTuOaqFAXRy0idcXlSbWFCxeSmJjIzTfffNK5119/HbPZzLhx4yguLmbkyJH897//rTxvsVj48ccfufPOO+nTpw++vr7ceOONTJ48uS6fgoiIiIiIiIiIiIicB37LzMXAXqUW6ekOQLSXB9FeHoxoElg5LqW4hG4rdnCgsJis0jKC3F1+q11E6oDLf9NHjBiBYRinPOfl5cU777zDO++8c9rr4+LimDt3rrPCExEREREREREREZFG4reMPAAGBvudcVyUpwdxXh4cLiphc24hg0L86yI8EXGxerGnmoiIiIiIiIiIiIiIKxmGwdLMXAC6rs0ge8FhrHklpx3fNcAHgE05BXUSn4i4nssr1UREREREREREREREXO1wUQlJRSW4GdBpeza51mxylybj2yMC//5NcWviXWV8V38fvk/LYlOukmoijYWSaiIiIiIiIiIiIiLS6C3NsFepdcoqw8cK7lG+lKbkk78qhfzVKXh3CMVvYDM8YwOAP1WqKakm0mio/aOIiIiIiIiIiIiINHrLyls/9jpuxSM+gPD7utHk1k54tQ0GAwq3nSD9v5tJe28zhTtP0MnXCzOQUlxKanGpa4MXOUeTJk3CZDLx4osvVjk+e/ZsTCYTAEuWLMFkMpGVlXXS9fHx8bzxxhsn/VxxzZkeS5YswWq18uKLL9KuXTu8vb0JCQmhV69efPjhh8582jWmSjURERERERERERERadSshsHyzDwAep0ow29UFCaTCa+WQXi1DKL0WD65y5Ip2JxOycEcThzcgVu4N617ebO7pJRNuQWM9Ax08bMQOTdeXl689NJL3H777QQHBztkzr59+5KSklL589/+9jdycnKYNm1a5bGQkBCeeeYZ3nvvPd5++2169OhBTk4O69atIzMz0yFxOIqSaiIiIiIiIiIiIiLSqG3OLSC7zIpfqUGHUjPeHZtUOe8e6UvIX9oSMDKevN+PkL/6GGVphbRLMbM71MymnAJGNlFSTaoyDAOjsNAla5u8vSsrzKpr+PDh7Nu3jxdeeIGXX37ZIXF4eHgQGRlZ+bO3tzfFxcVVjgH88MMP3HXXXYwfP77yWJcuXRwSgyMpqSYiIiIiIiIiIiIijdqy8v3UemSUEXBBBCb3U++c5BboSdCYFvgPaEbK86tpn1rC96FebMzRvmpyMqOwkN0XdHfJ2m03rMfk41OjaywWC88//zwTJkzgvvvuo1mzZk6K7mSRkZH8+uuv3HXXXYSFhdXZujWlpJqIiIiIiIiIiIiINGpL03MA6HXCiu/QyJPO2woLKTl0iOIDByg5cJDiA/uxGf1IyI4C7JVuhmHUuDJIpL654oor6Nq1K0899RRTp0495ZhTJdsKCs4tsfzaa69x1VVXERkZSYcOHejbty+XXXYZo0ePPqd5HU1JNRERERERERERERFptPKtVtblFoAJ+nl7YQm0kPXtLIr37Kb4wEFK9u+n9OjRk67zaO9Fa+8xuBuQWWblcFEJ8d6eLngGUl+ZvL1pu2G9y9aurZdeeomhQ4fy8MMPn/L8b7/9hr+/f5VjgwcPrvV6AAkJCWzbto3169fz+++/s2zZMi655BImTZrEhx9+eE5zO5KSaiIiIiIiIiIiIiLSaK3MyKPUBJGFNjp0a0rGtGmkv/Gfk8ZZAgPxaNkSjxbN8Wzegqzvl+NuQJvcMrYHuLEpp0BJNanCZDLVuAVjfTBw4EBGjhzJ448/zqRJk04637x5c4KCgqocc3M793ST2WymZ8+e9OzZk/vvv58ZM2Zw/fXX849//IPmzZuf8/yOoKSaiIiIiIiIiIiIiDRaiw+kA9A724b38CYc+8ccAALGjMand288W7TAo0UL3EJCqlxnzSugOKmEhCxPtgfAxtwCLo8IrvP4RZzhxRdfpGvXrrRt29ZlMSQkJACQn5/vshj+l5JqIiIiIiIiIiIiItJoLcvKAw8YEORPyYG9lBw4gMnDg8jJk7H4+Z32uqBxV3D0nz/QIbsrXwObc85tTymR+qRTp05MnDiRN998s07Wu+qqq+jXrx99+/YlMjKSgwcP8vjjj9OmTRvatWtXJzFUh9nVAYiIiIiIiIiIiIiIuMLR9Hz2etj/e1jXKHLm/gyA36BBZ0yoAXg0a4rZp4CEbBsAW/IKsRqGU+MVqUuTJ0/GZrPVyVojR45kzpw5XHLJJbRp04Ybb7yRdu3aMX/+fIe0lnSU+hOJiIiIiIiIiIiIiEgdWrT5CFigfRFERPqzf+5cwN76sTr8BiQQt8mGT5mNAmBPfhHt/bydGLGIc0yfPv2kY/Hx8RQXF1f+PHjwYIzTJI4PHTp0xp/PtA7Arbfeyq233lqdUF1KlWoiIiIiIiIiIiIi0ugYVoNlJ3IBGBDkS9G2bZQmJ2Py8cFv0KBqzRF42RDMJXm0y7FX82zMVQtIkfOZkmoiIiIiIiIiIiIi0ugU7jrBqgATAENbhZPzk71KzX/wYMw+PtWaw+Lthdk7nw7lLSA3aV81kfOakmoiIiIiIiIiIiIi0uhs3XiMdC8zHgZcGORLzs/2/dQCxo6p0Ty+vVqQkG0FYFNmrsPjFJH6Q0k1EREREREREREREWlUyrKK+C03D4AL/Xxgy2bKUlMx+/nhO2BAjebyG9iBDuVJtR0FRRTbbA6PV0TqByXVRERERERERERERKRRyV9zjNUhbgAMigj8o/Xj8OGYPTxqNJdbkCfRRglBJTbKTGa25xY6PF4RqR+UVBMRERERERERERGRRsOwGmStT2VDiAWAgYE+5MybB9S89WMFn46RJJTvq7Z2937HBCoi9Y6SaiIiIiIiIiIiIiLSaBTtymCryUq+m4lgNwstdmzDeuIElqAgfHv3rtWc3gkRlS0g1+0/5MBoRaQ+UVJNRERERERERERERBqN/DUprA61V6kNCPEn7+fy1o8jRmByd6/VnJ4tA0nIsSfVtpg9sRWqBaTI+UhJNRERERERERERERFpFMoyiyjak8nqUPt+agP9vcmdvwCAgDGjaz2v2cuNrn4+ACSGhZOyYOG5Bysi9Y6SaiIiIiIiIiIiIiJOdDSrkOfn7mRnSo6rQ2n08tceI88C24PslWo9DuzGmp2NpUkTfHr2PKe5o1sEE1FowzCZWL18lSPCFZF6Rkk1ERERERERERERESd6e/E+3l92gMve/p33lu7HajNcHVKjVbQrg/XBFqwmaO7tQUB568eAkSMxWSznNLdX66DKfdU2F5ZSkph4zvGK1JVJkyZhMpl48cUXqxyfPXs2JpMJgCVLlmAymSof3t7edOjQgffff/+Uc/3vY9SoUSet+8ILL2CxWHjllVcqj8XHx5/y+orHpEmTAFi6dClDhw4lJCQEHx8fWrduzY033khJSYmD/3T+oKSaiIiIiIiIiIiIiBP9vu84ACVWGy/8vItrP1hFcmaBi6NqfKz5pZSm5P/R+jHAh9yF9jaNAWPHnPP8HjH+dMi3J0x3t+pA1rezznlOkbrk5eXFSy+9RGZm5hnH7d69m5SUFHbs2MHtt9/OnXfeyaJFi6qMGTVqFCkpKVUeM2fOPGmujz76iEcffZSPPvqo8tjatWsrr/n222+rrJmSksJ//vMfduzYwahRo+jRowfLli1j69atvPXWW3h4eGC1Wh3wp3FqSqqJiIiIiIiIiIiIOElSRgGHTxRgMZt46pIEfDwsrDmYweg3fmPWhmQMQ1VrdaX4QBYYsCbCHYAeRw9jy8/HLSoK765dz3l+k8VMVz9vAHbHtSD7u+8wnHhzX+o/wzCwlVhd8qjNZ8vw4cOJjIzkhRdeOOO48PBwIiMjad68Offddx/Nmzdnw4YNVcZ4enoSGRlZ5REcHFxlzNKlSyksLGTy5Mnk5OSwYsUKAMLCwiqvCQkJqbJmZGQkgYGBzJ8/n8jISF5++WU6duxIy5YtGTVqFB988AHe3t41fu7V5ea0mUVEREREREREREQauRX77VVqXWOCuKlfc4a0DeeBrzaxMTGLB7/azKKdafzrio4E+Xi4ONLzX/H+bI55mTjkZcIMdJo/FwMIGDUKk9kx9SfdmgVB6QlSAv05XlBI1PLl+A0a5JC5peExSm0cfXKFS9aOntwXk0fNWppaLBaef/55JkyYwH333UezZs3OON4wDObNm0diYiK9evWqcYxTp07l2muvxd3dnWuvvZapU6fSt2/fal0bGRlJSkoKy5YtY+DAgTVeu7ZUqSYiIiIiIiIiIiLiJL/vOwFAv1ZNAIhv4svXt/fhwYvaYDGb+GlrCiPfWMbyvcddGWajULwvizUh9iRDV18vWLAAgIAx5976sUJY6xDi8uzVabuatyHrm28dNrdIXbjiiivo2rUrTz311GnHNGvWDD8/Pzw8PBg7dixPPfXUSYmtH3/8ET8/vyqP559/vvJ8Tk4O33zzDddddx0A1113HV999RV5eXnVinP8+PFce+21DBo0iKioKK644grefvttcnJyavGsq8/llWpHjhzhscce4+eff6agoIBWrVoxbdo0evToAdgznU899RQffPABWVlZ9OvXj3fffZfWrVtXzpGRkcG9997LnDlzMJvNjBs3jv/85z/4+fm56mmJiIiIiIiIiIhII2cYRmWlWr+WoZXH3Sxm7hvWmkFtwnjgy00cOJ7PdVNXc1O/eB4b1Q4v95pVl8jZlWUXU3a8kDWdvQDolZWOUViIe0wMXh07OGwdt3AfOhTCYT/Y064Hvb+bStmJE7iFhp79YjnvmNzNRE+uXuWVM9aurZdeeomhQ4fy8MMPn/L8b7/9hr+/P8XFxaxZs4Z77rmHkJAQ7rzzzsoxQ4YM4d13361yXUUrR4CZM2fSsmVLunTpAkDXrl2Ji4vjyy+/5JZbbjlrjBaLhWnTpvHcc8/x66+/snr1ap5//nleeukl1qxZQ1RUVG2e+lm5tFItMzOTfv364e7uzs8//8yOHTt49dVXq/TVfPnll3nzzTeZMmUKq1evxtfXl5EjR1JUVFQ5ZuLEiWzfvp0FCxbw448/smzZMm677TZXPCURERERERERERERAHan5nI8rwRvdwvdYoNPOt8lJoif7hvA9b3jAJj2+yGe/XFHXYfZKBTvy8IA1jWx76fWZcUywF6lZjKZHLaOyWSiq5c9cbenbScoKyN79myHzS8Ni8lkwuxhccnjXN7XAwcOZOTIkTz++OOnPN+8eXNatWpFhw4duOmmm7j++uv517/+VWWMr68vrVq1qvL4c1Jt6tSpbN++HTc3t8rHjh07+Oijj2oUa9OmTbn++ut5++232b59O0VFRUyZMqXmT7qaXFqp9tJLLxETE8O0adMqjzVv3rzyvw3D4I033uCJJ57gsssuA+CTTz4hIiKC2bNnc80117Bz505++eUX1q5dW1nd9tZbbzFmzBj+/e9/Ex0dXbdPSkRERERERERERAQqWzpe2DwED7dT1zd4e1h49vKOdIsN4sGvNrNwZyrPXd7RoYkegeL9WRz2NXPcHTxNJpp/PwuAgDGjHb7WBVGBkHecnWHBGMDx994n4JJLcA8Pd/haIs7y4osv0rVrV9q2bXvWsRaLhcLCwmrPvXXrVtatW8eSJUuqJNoyMjIYPHgwu3btol27djWOOTg4mKioKPLz82t8bXW5tFLthx9+oEePHowfP57w8HC6devGBx98UHn+4MGDHDt2jOHDh1ceCwwMpFevXqxcuRKAlStXEhQUVJlQAxg+fDhms5nVq1efct3i4mJycnKqPEREREREREREREQcacX+iv3Uzt76b0ynKNzMJlJzijmSVf2b03J2hmFQvD+LteX7qXUrLcCjsACPli3xbNPG4et1a9MEi80gw8NMdp/B2HJySH32OYevI+JMnTp1YuLEibz55psnnUtLS+PYsWMcPnyYr7/+mk8//bSyMKpCcXExx44dq/I4ftz+RYOpU6dy4YUXMnDgQDp27Fj5GDhwID179mTq1Klnje+9997jzjvvZP78+ezfv5/t27fz2GOPsX37di655BLH/CGcgkuTagcOHKjcH23evHnceeed3HfffXz88ccAHDt2DICIiIgq10VERFSeO3bsGOH/k+F3c3MjJCSkcsz/euGFFwgMDKx8xMTEOPqpiYiIiIiIiIiISCNWarWx+oA9qda3ZZOzjvdyt9ChaSAA6w9nOjW2xqbsRBHW7BLWhdobt3XZsQWwV6k5oyLQN9CL1uW7FyVdczu4uZG7YAE5v8xz+FoizjR58mRsNttJx9u2bUtUVBStWrXiscce4/bbb+ett96qMuaXX34hKiqqyqN///6UlJQwY8YMxo0bd8o1x40bxyeffEJpaekZY7vwwgvJy8vjjjvuoEOHDgwaNIhVq1Yxe/ZsBg0aVPsnfRYubf9os9no0aMHzz//PADdunVj27ZtTJkyhRtvvNFp6z7++OM8+OCDlT/n5OQosSYiIiIiIiIiIiIOszkpi/wSK8E+7iREBVTrmu6xwWxOymLD4Uwu69rUyRE2HsX7srAB65vYb4d3XGhPbgWMHuO0NTu7e7CLUrbY3Bl861858e4Ujj37LL69e2EJCnLauiK1NX369JOOxcfHU1xcXPnz4MGDMQyjWnOdar4KFRVrp/Loo4/y6KOPnnXNbt268emnn541FkdzaaVaVFQUCQkJVY61b9+exMREACIjIwFITU2tMiY1NbXyXGRkJGlpaVXOl5WVkZGRUTnmf3l6ehIQEFDlISIiIiIiIiIiIuIov+/7o0rNbK5eNdQFcUEArE9UpZojFe/PYp+/mSwL+Bg22u3fg2f79ni2aO60Nbs18Qdgc0kxoXfcgUfLllhPnCD1hRedtqaIOJ9Lk2r9+vVj9+7dVY7t2bOHuLg4AJo3b05kZCSLFi2qPJ+Tk8Pq1avp06cPAH369CErK4v169dXjvn111+x2Wz06tWrDp6FiIiIiIiIiIiISFW/77dXYvStxn5qFbrHBQOwMyWX/OIyp8TV2Bg2+35q68r3U+t0YA9uNisBY0Y7dd0ezUMA2OFtwppjJeq5Z8FkIvv778n77Tenri0izuPSpNoDDzzAqlWreP7559m3bx+ff/4577//PnfffTcAJpOJ+++/n+eee44ffviBrVu3csMNNxAdHc3ll18O2CvbRo0axa233sqaNWv4/fffueeee7jmmmuIjo524bMTERERERERERGRxqigpIyN5dVm/VudfT+1ClGB3kQHemG1GWxOznJSdI1L6bF8bAVlrCtv/dhl8wbcoqIIvuYap67bPtAXLxvku5vYufcEPt26EXz9dQCkPPUU1rx8p64vIs7h0qRaz549+e6775g5cyYdO3bk2Wef5Y033mDixImVYx599FHuvfdebrvtNnr27EleXh6//PILXl5elWM+++wz2rVrx7BhwxgzZgz9+/fn/fffd8VTEhERERERERERkUZuzcEMSq0GTYO8iQ3xqdG1F5RXq21MzHJCZI1P8f4sykywIdheqXbB7u1EP/8vLP7+Tl3XzWwiwWRP5K0/mgVA+P334960KWVHU0h/7TWnri8izuHm6gAuvvhiLr744tOeN5lMTJ48mcmTJ592TEhICJ9//rkzwhMRERERERERERGpkd/32Vs/9msVislUvf3UKnSPC+bHLSmsP6x91RyheH82u/3N5FlM+Bbk03NAH3zLtxZytm7BvmzIymZzYRFGqQ2zjw9Rz04m8eZbyPz8cwLGjManR486iUVEHMOllWoiIiIiIiIiIiIi55vf950AoF8NWj9WqNhXbUNiJjab4dC4GhvDaqP4QDbrQuy1Jd2OHCLqoQfrbP0+TYMA+D3EQtF+e5LUt29fAsddCUDKE//EVlxcZ/GIyLlTUk1ERERERERERETEQTLyS9iRkgNA35Y1T6q1jwrAy91MVkEpB45r361zUZKch1FiZW2wPTk5pFMCZp+ateM8F0NCA/A0INnHzOa9xyuPRzz2GG5hYZQcOsTxt9+ps3hE5NwpqSYiIiIiIiIiIiLiICv225MnbSP8CfP3rPH17hYzXZoFAbBBLSDPSeHWFEpNsDnYHYAhndrV6fq+FgsDvLwA+CUrF8OwJ/csAQFEPvUkACc++ojC7dvrNC4RqT0l1UREREREREREREQc5FxaP1a4oLwFpPZVqz3DMMhbuo3tgRYK3S2EuFlo5+tV53FcHGt/H/waYKIstaDyuP/w4fiPGgVWKylP/BOjtLTOYxORmlNSTURERERERERERMRBKirV+rUKrfUc3WPLk2qJSqrVVvb3P2EQzLoQCwB9g/0wm0x1HsfIiCAsBuwJsLB7Z3qVc5FP/ANLYCDFO3dy4qNpdR6biNSckmoiIiIiIiIiIiIiDpCUUcDhEwVYzCYubB5S63kqKtX2peWRVVDiqPAajdLUNNLfnYnJ4s66UPst8P7B/i6JJdjdjQstHgD8nJpV5ZxbkyaEP/53AE58+CG2goL/vVykTq1cuRKLxcLYsWNrfO3TTz9N165da3zd9OnTMZlMtG/f/qRzX3/9NSaTifj4+Crjg4KCTjvfpEmTMJlMmEwmPDw8aNWqFZMnT6asrKzGsZ2KkmoiIiIiIiIiIiIiDlBRpdY1Jgh/L/dazxPi60GLJr4AbEzKckRojYZhGKT88wnMPrEUm2FL+X5q/YL8XBbTmGh7knShRxnWvKpJ0sBLL8U9JgZbbi45c+e6IjyRSlOnTuXee+9l2bJlHD16tM7W9fX1JS0tjZUrV54UT2xsbI3nGzVqFCkpKezdu5eHHnqIp59+mldeecUhsSqpJiIiIiIiIiIiIuIAyyv2U2tZ+9aPFSqq1TZoX7Uayfr6a/KX/YZbRAJbgyyUmCDCw41WPp4ui2lsjP39sCXIQtLO41XOmcxmgq+5GoDMz2diGEadxyfOYxgG+VarSx41fS/l5eXx5ZdfcueddzJ27FimT59eee5U1WGzZ8/GVN5Sdfr06TzzzDNs3ry5skqs4vrExEQuu+wy/Pz8CAgI4C9/+QupqalV5nJzc2PChAl89NFHlceSk5NZsmQJEyZMqNHzAPD09CQyMpK4uDjuvPNOhg8fzg8//FDjeU7FzSGziIiIiIiIiIiISL1yLLuIpMwCesbXvg2hVJ9hGKwsr1Tr26rJOc/XPS6Yb9Yns15JtWorST5C2osvgZs3lqC4yv3U+gX7V978d4VoLw862yxsMVv5OTmDO3tGVzkfeOWVpP/nTYp27KBo61a8O3d2UaTiaAU2Gy2XbXXJ2vsHdsLXYqn2+K+++op27drRtm1brrvuOu6//34ef/zxav3uXH311Wzbto1ffvmFhQsXAhAYGIjNZqtMqC1dupSysjLuvvturr76apYsWVJljptvvpnBgwfzn//8Bx8fH6ZPn86oUaOIiIio0fM+FW9vb06cOHHO84Aq1URERERERERERM4rh0/k8/isLQx4+VfGT1nJgh2pZ79Iztnu1FyO55Xg7W6hW2zQOc/XvbxSbVNSFmVW2znP1xgcf+cdbAUF+PQeDZhYF2Hfy6y/C1s/VhgdFgjAfFsxRlnV19MtOBj/USMByJz5RZ3HJgL2VovXXXcdYG+fmJ2dzdKlS6t1rbe3N35+fri5uREZGUlkZCTe3t4sWrSIrVu38vnnn9O9e3d69erFJ598wtKlS1m7dm2VObp160aLFi345ptvMAyD6dOnc/PNN5/TczIMg4ULFzJv3jyGDh16TnNVUKWaiIiIiIiIiIjIeWBPai7/XbyPHzYfxfanrl9LdqdxUcK5f9Nfzmz5XnuVWs/mIXi6Vb865HRahfnh7+VGblEZu47l0rFp4DnPeT6zZmdX7knm0+cSsg6Uss3XXmHTL9j1SbVLWoTz0okM1gSZSdufSUTbqi1Cg6+9lpwf5pAzdy4Rjz2K5X9a7UnD5GM2s39gJ5etXV27d+9mzZo1fPfdd4C9HePVV1/N1KlTGTx4cK1j2LlzJzExMcTExFQeS0hIICgoiJ07d9KzZ88q42+++WamTZtGbGws+fn5jBkzhrfffrvG6/7444/4+flRWlqKzWZjwoQJPP3007V+Hn+mpJqIiIiIiIiIiEgDtjU5m7cX72Xe9j8q0ga3DaNT00De+nWf2gfWkRX7HbefGoDZbKJbbDDL9qSzITFTSbWzyP7+B4ziYjzbtMGa486mIBtlJmjm5U6ct+v2U6vQys+LlmUm9rvB/APpXP8/STXvrl3xbNeO4l27yPpuNqE3TXJNoOJQJpOpRi0YXWXq1KmUlZURHf1Ha1LDMPD09OTtt9/GbDaftEdbaWmpw+OYOHEijz76KE8//TTXX389bm61S2ENGTKEd999Fw8PD6Kjo2s9z6mo/aOIiIiIiIiIiEgDtOZgBjd8tIZL3l7OvO2pmEwwumMkP97bn+k3XcgNfeIBe1vC7ELH3/yUP5Rabaw+UJ5Uc8B+ahW6x9pbQCoxemaGYZD51ZcABI67lrLUAtaG2hMZ/YP8XRlaFaMC7BVzvxQUnJSgMJlMBF9zDQBZX3yBYVPLT6kbZWVlfPLJJ7z66qts2rSp8rF582aio6OZOXMmYWFh5Obmkp+fX3ndpk2bqszj4eGB1Wqtcqx9+/YkJSWRlJRUeWzHjh1kZWWRkJBwUiwhISFceumlLF269JxaP/r6+tKqVStiY2MdmlADVaqJiIiIiIiIiIg0OE//sJ3pKw4BYDGbuLRLNHcNbknriD8SCGH+nsSF+nD4RAEbEjMZ0jbcRdGe/zYnZZFfYiXYx52EqACHzVuxr5qSamdWuH49Jfv2Y/L2xrNtXwp3HmZ9+X5q9aH1Y4VLWkXwzpZcfg8wkZuaT0Bk1dgCL7mYtFdeoeTwYQpWrcK3b18XRSqNyY8//khmZia33HILgYFVK2LHjRvH1KlTmTdvHj4+Pvzf//0f9913H6tXr2b69OlVxsbHx3Pw4EE2bdpEs2bN8Pf3Z/jw4XTq1ImJEyfyxhtvUFZWxl133cWgQYPo0aPHKeOZPn06//3vfwkNPX3Vr9VqPSmp5+npSfv27Wv1Z1ATqlQTERERERERERFpQDLzS/hk5SEArr0wlsUPDeb1q7tWSahVqEzKHFJSxpl+32evUuvTMhSz2eSwebvEBGI2QXJmIWk5RQ6b93yT+eVXAASMHUPp0WLy3GCnd/l+akH1J6nWJcSXqDIocjOxaFfqSefNvr4EXnYZAJkzZ9Z1eNJITZ06leHDh5+UUAN7Um3dunUkJyczY8YM5s6dS6dOnZg5c+ZJe5SNGzeOUaNGMWTIEMLCwpg5cyYmk4nvv/+e4OBgBg4cyPDhw2nRogVffvnlaePx9vY+Y0INIC8vj27dulV5XHLJJbV6/jVlMv63zrQRysnJITAwkOzsbAICHPdNEhEREREREREREUf7bmMyD3y5mXaR/vxy/8Azjv18dSL/991WercI4Yvb+tRRhI3PX6asZM2hDP51RUcm9opz6Nyj//MbO1NymHLdBYzqGOXQuc8HZZmZ7Bs0GKOkhPivvybr50IWW0p58AIfWnh7sqK38ytXauLvy3Yz3VrIpdnw/uVdTzpfvHcvBy65FCwWWv26CPeIiLoP8jxTl/f/i4qKOHjwIM2bN8fLy8upa4nj1OR1U6WaiIiIiIiIiIhIA7JwZxoAw9uf/WZ7j3h7pdqmpCxKrdqjyRkKSsrYmGSvBOzX0nH7qVXoHhcEqAXk6WR/NxujpASvhATcm7bCmlHE2lD7rkf961Hrxwpjm4cBsMTHoCi35KTznq1b49OjB1itZH31dV2HJyJnoaSaiIiIiIiIiIhIA1FSZmPZ7nQAhrU/+x5prcL8CPByo6jUxo6jOc4Or1FaczCDUqtB0yBv4kJ9HD6/9lU7PcMwyPrK3vox6OqrKd6fBcCG8v3U+taj1o8V+jYLJrjUIMfdxG87T24BCRB07TUAZH39NUZpaV2GJyJnoaSaiIiIiIiIiIhIA7H2UAa5xWU08fOkS7Ogs443m030iA8BYJ2SMk7x+77jAPRrFYrJ5Lj91Cp0j7W/ftuO5FBUanX4/A1Zweo1lBw6hNnHB/8xY8hblUKWu4nd5d3b+tXDSjWLycQwsycAc9NO/TsZcNFFWEJDKUtLI/fXxXUZnoichZJqIiIiIiIiIiIiDcSCHfbKlqHtwjCbq5fA+aPSKcNpcTVWhmFUtuPs18rxrR8BYkK8aeLnQYnVxvaj2U5Zo6HK/PILAAIuvYSSQ0WUHsljQ7g7AO18vQjzcHdleKc1pqk9UbrQrQxr2cmJUpOHB0FXXQVA5hcz6zQ2ETkzJdVEREREREREREQaAMMwWLTLnlQbVo391Cr0KE+qrTuUiWEYTomtsdp6JJuDx/PxcjfX6DWpCZPJxAWxagH5v8pOnCB34SIAgsb/hZwFhwDY1N4fgH71sPVjhSGtw/ApM0j3NLNmz/FTjgn+y3gwmShYuYriAwfrOEI5V/qsbVhq8nopqSYiIiIiIiIiItIA7E3LIymjEA83MwNaV78qqktMEO4WE2m5xSRnFjoxwsbn+01HARjePgI/TzenraN91U6WNWsWlJbi1bkztuJQytIKMXm7sdbPXsHZvx62fqzg7WZhUJn9/TI36cQpx7g3bYrf4MEAZJVX5En95+5ur44sKChwcSRSExWvV8XrdybO+6QXERERERERERERh1m4016l1q9lKD4e1b+t5+VuoUN0IJuSslh3OIOYEB9nhdioWG0Gczbbk2qXdW3q1LUqkmobErMwDMMpe7c1JIbNRtZXXwMQNP5qchYmAlA4MJq9hZmYgD71uFINYHRYID/nZjDPVszk07ymwddeQ97ixWR9N5uw++/H7O3tgkilJiwWC0FBQaSl2dvC+vj4NPrf1/rMMAwKCgpIS0sjKCgIi8Vy1muUVBMREREREREREWkAFpXv3VWbNoM94oLtSbVDmVzRrZmjQ2uUVh84QVpuMYHe7gxqE+bUtTo2DcTdYiK9vNqwsSdG81espDQpCbO/P26R3bGuT8Ts587mNn6wJ5NOft4EudfvW98j20XgvvoEiV4mtidn0TEm+KQxvv37496sGaXJyeTM/ZmgcVe6IFKpqcjISIDKxJrUf0FBQZWv29nU708WERERERERERER4UReMRsS7a3/hrUPr/H1PeKD+XD5QbUPdKCK1o9jOkXi4ebcXXa83C10bBrIxsQs1h/ObPRJtawvvwQg4JLLyf3tmP2/h8bydXoWAP2D/V0VWrUF+nnSp9DEMl/48UD6KZNqJrOZ4GuuJu3fr5I5c6aSag2EyWQiKiqK8PBwSktLXR2OnIW7u3u1KtQqKKkmIiIiIiIiIiJSzy3enY5hQIfoAKICa94CrntcCAC7U3PJLiwl0Pvs+8bI6RWXWZm7LQWAS7s4t/Vjhe6xwZVJtcu71c2a9VFpahq5v/4KgGeH0RSszcMS5Mm2tv4s2ZKGmwlubBrq4iirZ1SAH8usecwrKODvpxkTeOWVpP/nTYq2baNw6za8O3Ws0xil9iwWS42SNdIwOPcrFCIiIiIiIiIiInLOFpXvp1ab1o8AYf6exIX6YBhUVrxJ7S3ZnU5uURmRAV5c2DykTtas2FetsVcbZs/6FqxWvLv3omh7EQABw2P5d6L9d+SayFDivD1dGWK1jW0bgdkw2OkFhzMLTjnGLSQE/9GjAMiYPr0OoxORU1FSTUREREREREREpB4rLrOybE86AMNr0fqxQmVS5lDjTso4wg/lrR8v6RKFxWyqkzUvKH/9dh3LIa+4rE7WrG8Mq5XMr78GwKfPtdgKynAL82ZTc1+WZ+XhbjLxt/jaJZ5dISLCn2559v+es+fYaceF3HAjmEzk/PQT+atW1VF0InIqSqqJiIiIiIiIiIjUY6sPZJBfYiXc35OO0YG1nqdHeQvIdYczHBVao5RbVMrC8srBy7rWXRvGiAAvmgZ5YzNgc1JWna1bn+QvX07Z0RQsoVGUptv3TfMfHssrh+0JqQlRIcR4ebgyxBob6WVv5zo3K/e0Y7w7diD42msBSHnqKWxFRXUSm4iczKVJtaeffhqTyVTl0a5du8rzRUVF3H333YSGhuLn58e4ceNITU2tMkdiYiJjx47Fx8eH8PBwHnnkEcrKGuc3NURERERERERE5PyzsLL1Yzjmc6iK6hFvr3TalJRFqdXmkNgao/nbUykus9EizJcO0QF1unZFteHaQ40zMZr55VcA+A27FaPYinuUL+ubebEqOx9Ps4m/xTWcKrUKl7QMx2QYbPAwSDyRf9pxYQ8+gFt4OKWHEzk+ZUodRigif+bySrUOHTqQkpJS+Vi+fHnluQceeIA5c+bw9ddfs3TpUo4ePcqVV15Zed5qtTJ27FhKSkpYsWIFH3/8MdOnT+fJJ590xVMRERERERERETlvWW0GM1Yd5tFvNjfa1nOuYBgGi3amATCs3bklDFqF+RHo7U5RqY0dR3McEV6j9P1me+vHy7o0xWSqm9aPFfq1CgXgq7VJlJQ1rsRoaUoKeUuWYPIMxGaNAcB/RBwvH7JXqV0fHUp0A6tSA4hvEUz38u3UvtyQdNpxFj8/Ip74BwAnPpxK8d69dRGeiPwPlyfV3NzciIyMrHw0adIEgOzsbKZOncprr73G0KFD6d69O9OmTWPFihWsKu8bO3/+fHbs2MGMGTPo2rUro0eP5tlnn+Wdd96hpKTElU9LREREREREROS8sTkpi8vf+Z0nZm/jq3XJ/LTlqKtDajR2HcvlSFYhnm5m+rVqck5zmc2mykqndYe1r1ptpOcW8/u+4wBc2jW6zte/rGtTwv09OZpdxKwNyXW+vitlfvkl2Gz4DJwEVgOPWH9WhrmxLqcAL7OJe2MbXpUagMlk4soI++/l90UF2IpO/6UF/4suwm/oUCgrI+XJpzBsjSuxKlIfuDyptnfvXqKjo2nRogUTJ04kMTERgPXr11NaWsrw4cMrx7Zr147Y2FhWrlwJwMqVK+nUqRMREX98YI4cOZKcnBy2b99+2jWLi4vJycmp8hARERERERERkaqyC0p5YvZWLv/v72w9kl15fGNiluuCamQWlbd+7N+qCd4elnOeryKptl77qtXK3K0pWG0GXZoF0ryJb52v7+Vu4baBLQD475L9lDWSNp4FGzZyYupHmHxCMfslAPYqtVcO2X8/bmzahAhPd1eGeE6u6NwUd5vBPj8z61aevlrNZDIR+c8nMPv4ULhxI1lffVWHUYoIuDip1qtXL6ZPn84vv/zCu+++y8GDBxkwYAC5ubkcO3YMDw8PgoKCqlwTERHBsWP2kt5jx45VSahVnK84dzovvPACgYGBlY+YmBjHPjERERERERERkQbMMAxmbUhm2GtLmLEqEcOAK7s15fkrOgFKqtWlheWtH4cnOKYKp0dFpdqhTAzDcMicjcn3m44AcGnXpi6LYUKvWEJ8PUjMKOCHzed/1WhpWhpH/vY3KC3Fb+gdYJjwbBXEb0FmNuUW4G02c09suKvDPCfBnu4M8fAC4NvkE9hKrKcd6x4VRdj99wOQ9uprlKal1UWIIlLOpUm10aNHM378eDp37szIkSOZO3cuWVlZfOXkDPvjjz9OdnZ25SMp6fTZfxERERERERGRxmRvai7XvL+KB7/azPG8ElqF+zHz1t68dnVXhifYb1zvScslt6jUxZGe/9Jzi9mcnAXAsHaOSRp0iQnC3WIiLbeY5MxCh8zZWCSeKGBDYhYmE1zSOcplcfh4uPHXAc0BeHvxPqy28zc5apSUcOT+ByhLT8czoSe4xwEQMCKOVw7aiypubtaEMI+GW6VWYXzrSAB+CTWTtybljGODJ07Aq1MnbLm5pD7/Ql2EJyLlXN7+8c+CgoJo06YN+/btIzIykpKSErKysqqMSU1NJTLS/gETGRlJamrqSecrzp2Op6cnAQEBVR4iIiIiIiIiIo1ZYYmVl37Zxej//Mbqgxl4uZt5dFRb5t43gD4tQwEI9/eiWbA3hgGbk7LPMqOcq8W70jAM6NwskPAAL4fM6eVuoUN0IADr1AKyRuaU7yXYt2Wow16P2rq+dxyB3u4cSM9n7tYzJ2AastSXX6FwwwbMAUH4Dr4HDPBKCOVXbxtb8wrxtZi5K6ZhV6lVuCgsEH9MpHqbWbopBaPs9K09TRYLUZOfAYuF3F9+IXfx4jqMVKRxq1dJtby8PPbv309UVBTdu3fH3d2dRYsWVZ7fvXs3iYmJ9OnTB4A+ffqwdetW0v5U4rpgwQICAgJISEio8/hFRERERERERBoiwzCY+OEq3l2ynzKbwfD24Sx4YBB3DW6Fh1vV20fdYu3tAzcmZroi1EZlYfl+asPaOab1Y4U/t4CU6jEMg9kb7a0fL+viutaPFfy93LmpXzwAb/+6D9t5WK2W/f33ZM6YASYzgde8SOnRYkweZvxHxvFyeZXarc3CCPVwc3GkjuFlMXNxRBAAcwMMCjadua2jV/v2hEy6EYBjzz6LLT+fr49lkF6iKmIRZ3JpUu3hhx9m6dKlHDp0iBUrVnDFFVdgsVi49tprCQwM5JZbbuHBBx9k8eLFrF+/nptuuok+ffrQu3dvAEaMGEFCQgLXX389mzdvZt68eTzxxBPcfffdeHp6uvKpiYiIiIiIiIg0GCsPnGBDYhY+HhY+uKEHH97Yk5gQn1OO7RYTBMDGpKy6C7ARKiq18tve4wAMa+/YSpwe8Uqq1dSuY7nsTcvDw2JmZMfTd8iqSzf1bY6fpxu7U3NZsDP17Bc0IEU7dpDy5FOAicBr/kVZmhncTITe0IF5plJ25hfhbzFze0yYq0N1qHFRIQAsjHTnxNJkjLMkS8Puvhv3pk0pO5rCsg8/5r6difRbvVOJNREncmlSLTk5mWuvvZa2bdvyl7/8hdDQUFatWkVYmP3D8PXXX+fiiy9m3LhxDBw4kMjISGbNmlV5vcVi4ccff8RisdCnTx+uu+46brjhBiZPnuyqpyQiIiIiIiIi0uB8tioRgMu7NeWihDNXRV0Q90elmmGcf9Ux9cXK/ScoLLUSFehFh2jHbl3SPc5+435PWi7Zhbr5Xh3fb7K3fhzSLoxA7/qxf1egjzs39rXvMfbWr3vPm9/HssxMku+9D6O4GL9RD2ArDAUzhF7bHveWgfy7vErttpgwgt3Pjyq1Cn2C/IjycCPX3cRSUymF246fcbzZx4fIp5/CAJ73CcEABhke58UecyL1lUs/db744osznvfy8uKdd97hnXfeOe2YuLg45s6d6+jQREREREREREQahbTcIuZtt9+kvq5X3FnHJ0QF4OFmJrOglMMnCohv4uvsEBulitaPQ9uFYzKZHDp3mL8ncaE+HD5RwIbETIa0PT/2pHIWm81gzmZ7Uu2yrq5v/fhnN/drzkfLD7HtSA5L9qQ3+NfSsFo5+vAjlB45gveFEzF5tQMgeHxbvDuE8l1qJnsKigh0s3Bbs/OrSg3AYjJxeUQw7yal83OUGyMWJ+HdqckZPwP8BgxgzS13saVVOzytBrcvP4G1SzGWQHVyE3GGerWnmoiIiIiIiIiI1K2v1iZRZjO4IDaIhGpURHm4melYPm5jktoHOoNhGPy6y76f0vD2jt1PrUL38orD9WoBeVbrEzM5klWIn6cbQ9vVr6RVqJ8n1/WOBeCtRQ2/Wi39P2+S//vveLQbjVv0IACCLm2Jb7dwymxGZZXanTFhBJ5nVWoVxkXYfzeXh7mRmZ5P0Z4z/44W5JfwZuf+ANywv5DYoCNKqIk4kZJqIiIiIiIiIiKNlNVmMHNNEgATq1GlVqFbbEULyCxnhNXobT+aQ0p2Ed7uFvq0DHXKGj3KW0CuO5zhlPnPJ99vOgLAyA6ReLlbXBzNyW4d2AIPNzMbErNYsf+Eq8OptZz58znx/vu4xfbDs90VAASMiMOvbzQAX6dmsL+wmGA3C389D6vUKnTw86aNjxclFhOLItzIXZx02rHWnBJe/34bRz1MhBfZuOrzV8j46EXK0tPrMGKRxkVJNRERERERERGRRmrJ7jSOZBUS5OPO2M5R1b6uW2wQABsSVeXkDIt22qvU+rdu4rQkTs94e2J0U1IWpVabU9Y4H5Rabfy0JQWAy7pGuziaUwv39+LanjEAvLlor4ujqTnDMMhfvYaUvz+OW/QFeF9wAwB+A5riP8T+vL5IOcGju5MBuCs2HD+3+pfcdBSTyVRZrfZztDslh3IoPph90riyE4Vsn7qZD8PsrSH/ER9Jk+7NiX7xRSxNmtRpzCKNSbVqZK+88soaTzxlyhTCw+tXObSIiIiIiIiIiPxhxqrDAIzv3qxGyZuKSrWdKbkUlljx9jh/b3C7wqJd9v3ULnJS60eAlmF+BHq7k11Yyo6jOXSJCXLaWg3Zwh2pZBaU0sTPg75Oqhp0hNsHteTzNYmsPpjBmoMZXNg8xNUhnZVRWkrOL/PI+PhjirZtwxKWgFfPWwETPj0iCBzTHAN4fv9R3kq0J5ovDQ/i9pjzt0qtwhURQbxwMIX1wW6keZrwXJxEWPPAyvMlKfkc/2grb8VZKHQzcYGPF39pF4XptddcGLVI41CtSrXZs2fj4eFBYGBgtR4//fQTeXl5zo5dRERERERERERqKSmjgCV77C3CJtSg9SNAdKAX4f6eWG0GW4+cXEEhtXfoeD5bkrMxmWCIE/fvMptNlfuqrTusisP/te1INnfOWM9dn28A4OLO0bhZ6m/Tr+ggb67q3gyAt36t39Vq1qwsjr//AfuGX0TKP56hNN2EZ4cr8Ol3LyaTBe9OTQi+sjWFNoNbtx+qTKg9EBfBlIQ4PMz193VwlFhvT3oF+mKYYF6UG8V7Mik5Yr/fXnwom/T3trDNZGVOU3cAnmsXg8lkcmXIIo1GtXdzfPPNN6tdefbNN9/UOiAREREREREROT/ZbAbZhaWcyC8mPbeEE/nFnMgrwc/TjSsvaKobgnVs5ppEDAP6t2pC8ya+NbrWZDJxQWwwv2w/xsbEzAZRFdNQfLLSXj04qE0YYf6eTl2re1wwv+5KY/3hDG7p39ypazUU6w5l8PbifSzZ/ceeVCM7RPDA8DYujKp67hzUiq/WJfPb3uNsSsqiaz2rPizas5+MGT9QuCUJs18zPDv/DbNv1TaFnm2CCbm6LamlZdyw9QBbcgvxMJl4tV0M4yMb1+fMlRHBrM7OZ14Lb64/VErukiR8ekSQMWMntlIbr/f0B+CqiGAuCKzZZ7iI1F61kmqLFy8mJKT6H1o///wzTZs2rXVQIiIiIiIiItLwTf/9IIt2pXE8r4QTecVk5JdQZjNOOTbA252LEpzX6k6qKimz8dW6JACu6x1bqzm6xQaVJ9WyHBhZ45ZfXMbX5a/LjX3jnb5ej4pKtUOZGIbRaBPbhmGwfN9x3v51H6sPZgBgNsElXaK5a3Ar2kb6uzjC6okN9eHyrk35dkMyb/+6lw9v7OnqkADI/H4Deb8lgTkQk7kHnu16/HHSBG5h3ng088cjPgDfbhFsLyzihq0HOVpcSoi7hWkdm9MryM91T8BFLgkP4h97k9nlbrDf10zLbccp3H4CbAZLugWy0ceGt9nM/7Wo/n6YInLuqpVUGzRoUI0m7d+/f62CEREREREREZHzw+akLJ6es+OU5wK93Qn186CJnyfZBaXsTs1l9sYjSqrVoXnbj3E8r4Rwf0+G1XLfrop91TYkNu6EjCPN2niE3OIymjfxZVBr5+8b1SUmCHeLibTcYpIyCokN9XH6mvWJzWawaFcaby/ex+akLADcLSbGXdCMOwa1JL6GFZz1wV1DWjJrYzILd6ax/Wg2HaIDz36Rkxg2g/R3FlJyxAuTW0j5sQI8mvrh3TkGj5gAPJr5Yfb64xb1vOPZ3LnjMAVWG619PJnRuQVx3s6t2KyvQtzdGBYawLzjOSzo7E/LldlgGJi6NuGNGBsU27gnNpxoLw9XhyrSqFS7/WOFQYMGccsttzB+/Hi8vb2dEZOIiIiIiIiINGCGYfDSL7sAGN4+nOv7xBPq60GYvyfBPh54uP2xH862I9lc/NZyFu5MJbeoFH8vd1eF3ajMWGVvMXjNhbG413KfqE5NA7GY7QmZlOwiooN0n+hcGIbBJysOAXBDnzjMZucnKb3cLXSLDWbNwQzmbDnK3UNaOX3N+uT/vtvKF2vtlYFe7mau6RnLbQNbNOj3csswPy7uHM2czUd5d8l+3p5wgUvisJWUcez5n7AVlSfTirbS5NaReHdoecrxhmEwJSmdyfuPYgADg/34oEM8ge41vn19XrkyIph5x3P4JdTMnUGe+HZqwkcJPhw5dIymnu7cGeu8fRdF5NRq/K+mbt268fDDDxMZGcmtt97KqlWrnBGXiIiIiIiIiDRQy/cdZ8X+E3hYzDx9aQcGtQmjY9NAIgK8qiTUADpEB9AizJfiMhvzt6e6KOLGZW9qLqsPZmA2wbUXxtR6Hm8PC+2j7G3xNiRmOiq8RmvF/hPsTcvD18PCVd2b1dm6V/ewvwc+X52I9TTtWc9HW5Oz+WJtEiYT3DGoJcsfG8rTl3Zo0Am1CrcOsO+Pt3BnKoUl1jpZ02b88d4py8zl6D++x1YUgmGzYvHbT7NXbz9tQg3g5YPHeKY8oXZDdCifdW7Z6BNqACNCA/GzmEkuLSP5jgQKhzfjrcQ0AJ5oGY1PLb8UISK1V+PfujfeeIOjR48ybdo00tLSGDhwIAkJCfz73/8mNVX/+BURERERERFpzGw2g5d/2Q3Adb3jaBZ85nZyJpOJy7rY92X/fvNRp8cn8NnqRACGtY8gKvDcEgjdYuwtILWv2rmbXl6lNq57szqt2BzbOYpAb3eOZBWydE9ana3rai/Ps1fTXt61KX8f3Y4mfudPi8FOTQNpGuRNUamN3/amO3WtMpvBP/cm88TeIwAU7UsmZfJCsIRjlBbi3T6PqCcmYbJYTjvHD2lZvH7Yfl/5qZbRvNSmGe51UKnZEHhbzIwJs7fw/DY1k3/tT6HQZqNngC+Xhwe5NjiRRqpWqWw3NzeuvPJKvv/+e5KTk5kwYQL//Oc/iYmJ4fLLL+fXX391dJwiIiIiIiIi0gD8vO0YW49k4+th4e4hp69K+LNLu0YD8Pu+46TnFjszvEavoKSMbzckA/ak57nqFhsEwEZVqp2TpIwCFu20JxVu6BNfp2t7uVsYX14ZN2NVYp2u7Sor95/gt73HcTObeGB4G1eH43Amk6lyj8r5O5xbBLE2J58Pko/z0ZHjTFm6ifR3NmHybIKtOIfA0UE0ueniM16/Pa+Qv+20v+/uiAnjzthw7c/4P8ZF2FtofpOayTep9s/aya2b6s9JxEXOqT50zZo1PPXUU7z66quEh4fz+OOP06RJEy6++GIefvhhR8UoIiIiIiIiIg1AqdXGv+fbq9RuHdiC0GpWfjRv4kuXZoFYbQZzt6Y4M8RGb87mo+QWlREb4sOAVk3Oeb5usfZKtW1Hcyguq5s2c+ejGasOYzNgQOsmtAr3q/P1J/SKBWDx7jSSMgrqfP26ZBhGZZXatRfGEht65mrahmpEB3tSbdHOVMqsNqet0yfIj/9rEQXAs2UGa6JCsBUdJ+zWBAKGX3jGa0+UlHHj1gMU2mwMDvbniRbRTouzIesf7Ee4hxsF5a/j+MhgugWcn+9bkYagxkm1tLQ0Xn31VTp27MiAAQNIT09n5syZHDp0iGeeeYYPP/yQ+fPnM2XKFGfEKyIiIiIiIiL11Nfrkjl4PJ8QXw/+OqBFja69rGt5C8hNR5wRmpSraP04oVcsZge0V4sP9SHYx52SMhs7U3LPeb7GqLDEyhdrkwC4sY6r1Cq0CPOjX6tQDAO+WHt+V6st3JnGxsQsvNzN3Du0lavDcZoL40MI9HYns6CU9YedV0lqGAZ/+fpXRh8pxmo28fcunhQ81A/vhOZnvK7UZnDb9kMkF5US7+3BlA5xuKnl4ylZTCauCLd/gcHHYuYfSj6KuFSNk2rNmjXjww8/5MYbbyQ5OZlvvvmGUaNGVSk37dy5Mz179nRooCIiIiIiIiJSfxWWWPnPoj0A3DOkFX6ebjW6/uLOUZhNsCExi8QT53eljKtsSc5iS3I2HhZzZbu/c2UymSqr1dQCsna+33SE7MJSYkK8GdIu3GVxXNfL3g70y7VJlJQ5r7LJlaw2g3/Ps1fT3tSvOeEBXi6OyHncLGaGtbe/n5zZAjJ77mrKjjfjiR0ldM4qItfDws1HTpBZWnbG657Zf4Tfs/LwtZiZ3qk5Qe41+zujsbk1Joxegb683KYZkZ51t+eiiJysxkm1RYsWsXPnTh555BHCwsJOOSYgIIDFixefc3AiIiIiIiIi0jB8vPIQqTnFNA3yZmLv2BpfHx7gRd+W9naEc7YcdXR4AnxWvl/WmE6R1W7NWR3dYoIA2JiY5bA5GwvDMJi+4hAAN/SOx+LCSp3hCRGE+3tyPK+E+TuOuSwOZ/ph8xF2p+YS4OXGHQOrt+djQzYiIRKA+TuOYRiGU9YIHN0Ls+cJfENzmTH6App6unOgsJhbtx2i1HbqNWemnODD5OMAvN0+lna+3k6J7XzSzMuD7y9ozVWRIa4ORaTRq3FSbcCAAc6IQ0REREREREQaqOyCUv67eB8AD17UBk83S63mubSrvaXV7I1HnHYDuLHKLizl+8321poTe8c5dO6KSrUNqlSrsdUHM9h1LBdvdwt/6RHj0ljcLWau6WmPYcaqwy6NxRlKymy8tsBeTXv7oJYE+pz/1T4D2zTB081MUkYhu445pz2ryWwi6qnLiHxoDOFeHnzauQU+FjPLs/L4v73JJ32Wb8jO57HdyQA8FB/B6LAgp8QlIuIs1UqqXXDBBWRmVv8fRv379+fIEfVAFxEREREREWkMpizbT05RGW0i/Li8W9NazzOqYyQebmb2puU57QZwYzVrQzJFpTbaRvjTIy7YoXN3jgnEZILkzELScoscOvf57uPyKrXLuzWtF0meay6MxWyCVQcy2Jd2fv0Ofrk2kaSMQpr4eXJTv3hXh1MnfDzcGNDaXgE8f7vzWkCa/lRhmeDnzZSEOEzAp0dPMPXI8cpzqcWl3LztECWGwegmgTwUH+m0mEREnKVaSbVNmzaxefNmtmzZUq3Hpk2bKC4udnbsIiIiIiIiIuJiqTlFTPv9IACPjGx3Tu3rArzcGdrWvgfQ95vUAtJRDMPgs9X21o/X9Y7FZHJsi8EAL3dah/sBsEktIKvtaFZh5V5XN/Z1bPVgbUUHeTOsfQRA5XvmfFBQUsabv9qrae8b1gofj8azf9efW0DW2ZpNAvlnS3vl8ZN7j/DriRyKbTZu3naQYyWltPX14q32sZgd/FkkIlIXqv03yLBhw6rdesHR/zgTERERERERkfrpP4v2UlRqo3tcMMPbh5/zfJd1jeaX7ceYs/koj45si9mFe0ydL7YdyWFfWh7e7pZzqiQ8k24xwexJzWNjUhYjOqj6pDpmrDqM1WbQu0UI7SIDXB1OpYm9YlmwI5Vv1yfz6Mh2eHvUrp1rfTJ9xSHSc4uJCfHmmp413/OxIRvWPhyzCbYfzSE5s4BmwT7VvtZmM/jn99sY170ZF8TWrML1zpgw9uQX8cWxDG7ffoh+wX6szykg0M3C9I7N8atlm2AREVerVlLt4MGDNZ64WbNmNb5GRERERERERBqOg8fz+XJtEgCPjWrnkC/ZDmkXjr+nG0eyClmfmEnP+JBznrOxW1BeoTKoTRj+Xs5pMXhBXBBfrktio/ZVq5aiUitflP/uTOob79pg/sfA1mHEhviQmFHAnM1H+UtP1+71dq6yC0qZsmQ/AA8Mb4OHW7Uad503Qv086REXwppDGSzYkcpN/ZpX+9oft6bw2epEvt90lJWPD63R54fJZOKlts04VFjMqux8fjmegxl4r0MczX08a/FMRETqh2r9LRIXF1fjh8WibxuIiIiIiIiInM9enb8bq81gSNswLmzumOSXl7uFkR3tlU7fb9J+7Y6wYGcaABclRDhtjW7lVSxbkrMps9qcts75Ys7mo2TklxAd6MXw9s57XWrDbDYxoZe9muuz1YddHM25e+9Pez5e1tU5lZr13YgO9vdYTfZVKy6z8sq8XQDcNrBFrRLynmYzH3ZsTqyXBwBPtIxmcEj9qcoUEamNxvXVDBERERERERFxiG1HsvlxSwpg30vNkS7rat+L56ctKZQqQXNOkjIK2JmSg9kEQ9ude3vO02kV5oe/pxsFJVb2pOY5bZ3zgWEYfLzyEADX9YnDzVL/bs+N794MD4uZzcnZbE3OdnU4tZaWW8S03w8B8PCItue052NDVpFQX3Mog6yCkmpd89mqRJIyCgnz9+SvA6pf3fa/mni4saBHG+Ze0Jo7Y8JqPY+ISH1R//7WFhEREREREZF676Vf7BUMl3WNJiHasZUHfVqE0sTPk8yCUpbvPe7QuRubhTvtlSk94kMI9vVw2jpms4kuMUEAbFALyDPakJjJtiM5eLiZ6+3+XqF+nozuZK8YnbGq4Varvf3rPgpLrXSLDXJqpWZ9FxfqS7tIf6w2g193pZ11fE5RKW/9uhewt8z08ajWDkKnFejuxgWBvg5pESwi4mpKqomIiIiIiIhIjWw/ms1ve4/jZjbx0EVtHT6/m8XMxZ2jALWAPFcLdtiTaiPqIKHQLTYIgI2JWU5fqyH7qLxy6rIu0YQ4MdF5rq7rHQfAD5uPkl1Y6uJoai4po4CZaxIBeGRk20af0Kn4DKhOC8gpS/aTWVBKyzBf/tKjmbNDExFpUJRUExEREREREZEa+am87eOw9uHEhvo4ZY2KFpDzd6RSUFLmlDXOd9kFpaw+mAE4dz+1CpVJtSRVqp3O1OUHK39/buwb79pgzqJHXDBtIvwoLLXy3YbkOls3u7CU7zYmczyvuNZzWG0G//x+G6VWgwGtm9C3ZRMHRtgwjehgrzxcuiedolLracelZBcydflBAB4b1a5eticVEXGlGn8qtmjRghMnTpx0PCsrixYtWjgkKBERERERERGpnwzDYO5We1JgTKcop63TNSaI2BAfCkqsLNx59nZlcrIle9Kw2gzaRPgRF+rr9PW6xgQDcCA9v9r7NjUmn69O5NkfdwDw4EVt6Ng00MURnZnJZKqsVvtsdSKGYTh1vcISK+8u2c/AlxfzwJebueb9VeQU1a5C7pV5u1myOx1PNzP/N6a9gyNtmDpEBxAd6EVhqfWMbXVfX7CH4jIbPeODG3XLTBGR06lxUu3QoUNYrSd/m6G4uJgjR9SSQURERERERJzDZjP4v++2ctO0NXy1LqnWN1vl3OxMyeXQiQI83MwMa++8G64mk6myWu0HtYCslfnlrR+HO/F1+rMQXw+aN7En7zYlZdXJmg3F7I1H+MfsrQDcPqgF9w5t5eKIqueKbk3x8bCwNy2PNeVVj45WUmbj05WHGPjKYl76ZRfZhaWYTLAvLY+/zdyI1VazZN7sjUeYsnQ/AC9f1Zn2UY7d87GhMplMlUmy+TuOnXLM7mO5fLPeXpX499HtG33LTBGRU6n2LpM//PBD5X/PmzePwMA/vk1jtVpZtGgR8fHxDg1OREREREREpMI3G5L5fLV9f5zFu9N5YvY2hrYN59Ku0QxtF46Xu8XFETYOFVVqg9uE4edZ7dsKtXJZ12je+nUfS3ank5lfQnA93n+qvikus7J0dzpQN60fK3SLCeLg8Xw2JmYxuG14na1bn/2y7RgPfb0Zw4Dre8fx91HtGkyywt/Lncu6RjNzTRIzVifSq0Wow+a22gy+33SE1xfuISmjEIBmwd48MLwNLcP9uPq9lSzenc7L83bx+OjqVZttSc7isW+3AHDn4JZc1rWpw+I9H4zoEMnHKw+zcKe9itVirvo+fOmXXdgMGN0xku5xwS6KUkSkfqv2v34vv/xywP6thhtvvLHKOXd3d+Lj43n11VcdGpyIiIiIiIgIQE5RKS//sguwJwgOHs9nX1oev2w/xi/bj+Hn6caIDhFc2iWa/q2aaA8YJ6mr1o8VWoX7kxAVwI6UHH7edowJvWKdvub5YvWBDPKKywj396RLs6A6W7dbbBCzNh5hoyrVAFiyO417Z27AajO4qnsznrm0Q4NJqFWY2CuOmWuS+GVbCsfzEmji53lO8xmGwbztqbw6fzd70/IACPP35L6hrbi6ZywebvbP73+P78K9Mzfy3tIDtI3w58oLmp1x3rScIm77ZD3FZTaGtgvn4RFtzynO89GFzUMI8HIjI7+E9YczubB5SOW5lftP8OuuNCxmE4+M1J+diMjpVPv/ZdhsNmw2G7GxsaSlpVX+bLPZKC4uZvfu3Vx88cW1DuTFF1/EZDJx//33Vx4rKiri7rvvJjQ0FD8/P8aNG0dqamqV6xITExk7diw+Pj6Eh4fzyCOPUFamDYxFRERERETOJ/9ZuJfjeSW0CPPlnQkXsOCBgcy9bwB3DGpJ0yBv8orLmLXhCJOmraXX84v497zdNW4ZJme3OzWXA8fzy1s/1k0VUkULyO/VArJGFpS3fhzWPgKzue6SON1i7dUtGxMzsZ1Hv4M2m8F1H66m34u/8s7ifdXaM27VgRPc/ul6Sq0GYztH8dK4znX6WjhKx6aBdI0JotRqMGPV4XOa60B6Hpe/8zt3zFjP3rQ8Ar3deWxUO5Y9MoTr+8RXJtQALukSzT1D7G0y/z5rKxsTM087b1GpldtnrOdYThGtwv34zzVdT6rCEnC3/NG2d8GfWkDabAYv/LwTgAkXxtIizM8l8YmINAQ1/urewYMHadKkCWBPejnC2rVree+99+jcuXOV4w888ABz5szh66+/ZunSpRw9epQrr7yy8rzVamXs2LGUlJSwYsUKPv74Y6ZPn86TTz7pkLhERERERETE9fam5vLxikMAPHlxAh5uZkwmEwnRAfx9dDt+e3QI39zRh+t7xxHi68GJ/BLeXryPOZuPujbw89DcrfabsANbh+Hv5V4na17SxZ5UW30wg+TMgjpZs6EzDIOFO+1JtRF12PoRoG2kP17uZnKLyth2NLtO13amtYcyWL7vOEeyCnll3m76vPArT/+wncQTp35PbkzM5JbpaykuszGsXTiv/6VhJ3luHdACgI+WHyS3lvtZGobBQ19vZnNyNj4eFu4d2opljw7hzsEt8fY4dfveBy9qw0UJEZSU2bjt0/WkZBeect4nZm9jY2IWAV5ufHBDjzr7fGqIRlTuq5aKYdgT3z9tTWFLcja+HhbuG9baleGJiNR7NU6q2Ww2nn32WZo2bYqfnx8HDhwA4J///CdTp06tcQB5eXlMnDiRDz74gODgP3r1ZmdnM3XqVF577TWGDh1K9+7dmTZtGitWrGDVqlUAzJ8/nx07djBjxgy6du3K6NGjefbZZ3nnnXcoKTn9N4aKi4vJycmp8hAREREREZH6xzAMnpmzgzKbwfD2Eafco8lsNtEjPoRnL+/I6v8bxu0D7Td/P1x+oPKGoTjGH60fI+tszeggb/q2tO/j9NXapDpbtyHbdiSHlOwifDws9GnpuD2wqsPdYmZ4eSXMO4v31enazjS7vFKyR1ww7aMCKCy1Mn3FIQb/ezF3fba+ShXV9qPZ3PjRGvJLrPRtGco7Ey+oUoHVEI3uGEnrcD9yisoqv+RQUwt2pLIxMQtvdwvzHxjIQyPaEuh95uSX2Wzi9au70jbCn/TcYm77ZD2FJdYqYz76/RDfrE/GbIK3J1xA8ya+tYqvsRjYJgwPNzOHTxSwJzWPkjIbr8zbDcBtA1sS5n9u7T1FRM53Nf4b/bnnnmP69Om8/PLLeHj8sUFwx44d+fDDD2scwN13383YsWMZPnx4lePr16+ntLS0yvF27doRGxvLypUrAVi5ciWdOnUiIuKPb12NHDmSnJwctm/ffto1X3jhBQIDAysfMTExNY5bREREREREnG/e9lSW7zuOh5uZJy9OOOt4d4uZ2we1xNPNzLYjOaw5mFEHUTYOe1Jz2ZeWh4fFzPA6rn669kL7XmpfrkuizGqr07UbogXlVWoDW4fh5X7qCiBn+tuw1phM9t/fbUcafrVacZmVn7bYE8oPjmjD3Pv6M+OWXgxqE4bNsFdwXvHfFYyfsoIv1iRyw9Q15BSV0T0umA9u6OGS18DRzGYT9wy1t2L8cPlB8oprtvWK1WZUJm5u7h9Ps2Cfal/r5+nGhzf2IMTXg61Hsnn02y2VX5hYtiedf/20A4B/jE1gYJuwGsXVGPl6ujGglb0L2fztx/hs9WESMwoI8/fkrwOauzg6EZH6r8ZJtU8++YT333+fiRMnYrH88Y+CLl26sGvXrhrN9cUXX7BhwwZeeOGFk84dO3YMDw8PgoKCqhyPiIjg2LFjlWP+nFCrOF9x7nQef/xxsrOzKx9JSfqmm4iIiIiISH1TVGrlufKbpbcNaEFsaPVuwob4enDlBc0A+81fcYyKKrUBrZsQUMet1UZ2iCTU14PUnGIW7Uqr07Uboor91C6q4+RnhdYR/lxa3rbzjYV7XBKDIy3elU5OURmRAV70bh6KyWSif+smfHzzhfxy/wCu6t4Md4uJtYcy+fusrZzIL6Fj0wA+mtQTX083V4fvMBd3jqZFE1+yCkr5dGXN9labtSGZvWl5BPm4c/ugljVeOybEh/9OvAA3s4k5m4/yzuJ9HDyezz2fb8BmwFXdm3Fzv/gaz9tYjehg/2z4YfNR3vrVXlF6//DW59X7VUTEWWqcVDty5AitWrU66bjNZqO0tPo9lZOSkvjb3/7GZ599hpeXV03DOCeenp4EBARUeYiIiIiIiEj98v6yAyRnFhIZ4MVdQ2p2E/aW/vEALNyZyqHj+U6IrvGpSKqN7hRV52t7uJm5qoc9UTpzTWKdr9+QJGUUsDMlB7MJhrY7uV1qXfnbsNaYTbBwZxqbk7JcFocjfF/e+vGyrtGY/2dftHaRAfx7fBeWPzaUuwa3JNDbnY5NA/jk5l5nbW3Y0FjMJu4eYr8n+MFvBygoqV61WlGplTcW7gXgrsEta52U790ilMmXdQTg3/P3cO37q8gpKqNbbBD/uqIjJlPD3bOurg1rH4HJBHvT8sjIL6FFmC9X91AnLxGR6qhxUi0hIYHffvvtpOPffPMN3bp1q/Y869evJy0tjQsuuAA3Nzfc3NxYunQpb775Jm5ubkRERFBSUkJWVlaV61JTU4mMtPduj4yMJDU19aTzFedERERERESkYUrOLOC/S+zfnv+/se3x8ajZt+dbhfszuG0YhgHTa7n/j/xhX1oue1LzcLeYXFb9dG1PewvIpXvSScoocEkMDcHC8taPPeJDCPb1OMto52kR5scV3eyJ0NcWNNxqtezCUhbttFdHXta16WnHRQR48eiodmx68iJ+uLs/IS78s3emy7pGExviQ0Z+CZ+tql6Ce8aqwxzJKiQq0Isb+sSf0/oTesVyY584AI7lFBEZ4MV713XH063ht9isS038POkRF1z582Oj2uFmadj7/omI1JUaf1o++eST3HPPPbz00kvYbDZmzZrFrbfeyr/+9S+efPLJas8zbNgwtm7dyqZNmyofPXr0YOLEiZX/7e7uzqJFiyqv2b17N4mJifTp0weAPn36sHXrVtLS/mj9sGDBAgICAkhIOHuvfREREREREamfXpi7i6JSGxc2D+GSzrWrjPpr/xYAfLUuiezC6ndWkZPN3WrfYqF/qyYuq76Jb+JL/1ZNMAz4cq22cTiditaPI1yU/Pyz+4a1wmI2sXRPOusPN8z9DX/ZlkKJ1UbbCH/aR/mfdbzJZDqpmu184mYxc095tdp7yw5QWGI94/jcolLeWfxHe0FH7C/3z4sTGN0xkjB/T96/oTvhAXXbAet8Mbqj/e/WHnHB9eLzQkSkoahxUu2yyy5jzpw5LFy4EF9fX5588kl27tzJnDlzuOiii6o9j7+/Px07dqzy8PX1JTQ0lI4dOxIYGMgtt9zCgw8+yOLFi1m/fj033XQTffr0oXfv3gCMGDGChIQErr/+ejZv3sy8efN44oknuPvuu/H09KzpUxMREREREZF6YMX+4/y0NQWzCZ6+pEOtW3r1axVKu0h/CkqsfKGWgefEla0f/+zaC+3Val+uS6LUanNpLPVRdkEpqw/ak1euqij8s7hQX64q39/w9QV7XRxN7Xy3sbz1Y7dotRcsd8UFTWka5M3xvOKztmP94LeDZBaU0iLMl3Hl74Vz5WYx8+513Vn1+DA6NwtyyJyN0Q194njlqs68d313vbdFRGqgVnW9AwYMYMGCBaSlpVFQUMDy5csZMWKEo2Pj9ddf5+KLL2bcuHEMHDiQyMhIZs2aVXneYrHw448/YrFY6NOnD9dddx033HADkydPdngsIiIiIiIi4nxlVhvP/LADgIm94kiIrv0e2CaTiZv7NwfsLSCVhKmd/el57DqWi5vZ5PJqhosSImji50F6bjGLdqae/YJGZsmeNKw2gzYRfsSF+ro6HADuGdoKd4uJ5fuOs/rACVeHUyNHswpZdcCepDxT68fGxt1irtxbbcrS/RSVnrpaLT23mA9/OwDAIyPaOry9oOU8rgisC24WM+N7xBDqp8IEEZGaqFfNcpcsWcIbb7xR+bOXlxfvvPMOGRkZ5OfnM2vWrJP2SouLi2Pu3LkUFBSQnp7Ov//9b9zcatZrX0REREREROqHGasOszs1lyAfdx68qM05z3dpl2ia+HmQkl3Ez9uOOSDCxufn8iq1fq2aEOTj2n2iPNzsN4EBPlut6sP/Nb+89ePw9q6vUqsQE+LDX8pfs9cW7MEwDBdHVH0/bD4KQK/mITQN8nZxNPXLuO5NiQ70Ii23mK/Wnbod6zuL91FQYqVLs0BGdYw85RgREZGGpsZJteDgYEJCQk56hIaG0rRpUwYNGsS0adOcEauIiIiIiIicx07kFfPagj0APDyiLcG+557A8XK3cH3veACm/nagQd3Qry9+Kt9PbUyn+nFT/Nqe9haQv+09TlJGgYujqT+Ky6ws3Z0O1I/Wj39295BWeFjMrD6Ywcr9DadabXZ568fLu6lK7X95ulm4c3BLAN5dsp/isqrVakkZBXy2+jAAj41qp/aCIiJy3qhxUu3JJ5/EbDYzduxYnnnmGZ555hnGjh2L2Wzm7rvvpk2bNtx555188MEHzohXREREREREzlP/nr+bnKIy2kcFVO6d5QgTe8fi4WZmc3I26w9nOmzexuDg8Xx2puRgMZsYkVA/kmqxoT4MaN0E4Kz7OTUmqw9kkFdcRri/J13q2T5T0UHeTOhl/51uKNVqO1Ny2HUsFw+LmTEdXbuXYH01vkcMEQGepGQX8c365CrnXl+wh1KrwYDWTejbqomLIhQREXG8GifVli9fznPPPcenn37Kvffey7333sunn37Kc889x/r16/nggw945ZVXePPNN50Rr4iIiIiIiJyH9qXl8sVaewuxZy7t4NC9cpr4eXJF+X5IU5cfdNi8jcHc8taPfVuGOqRy0FEmlCddv1qXrL3yyi0ob/04rH0E5nq419Sdg1vi6WZm3eFMftt73NXhnNXsTfYqtSHtwgj0cXdxNPWTl7uFOwbZq9X+u3g/JWX238WdKTl8V/7n9+jIdi6LT0RExBlqnFSbN28ew4cPP+n4sGHDmDdvHgBjxozhwIED5x6diIiIiIiINAqfr07CMGB4+3AubB7i8PlvGdAcgHnbj6llYA1UJNXGdKpflTrDEyII8/fkeF5xZTKpMTMMg4U77X8OFyWEuziaU4sI8OK63nFA/a9Ws9kMfthk30/tCrV+PKNrL4yliZ8nR7IK+W6jvVrt3/N2YxgwtnMUnZoFujhCERERx6pxUi0kJIQ5c+acdHzOnDmEhNj/j09+fj7+/v7nHp2IiIiIiIic94pKrcwqvxk7sVecU9ZoE+HPgNZNsBkw7fdDTlnjfHP4RD7bj9pbP47sUD9aP1Zwt5j5S49mAHy+Wi0gtx3JISW7CB8PC31b1t9We3cMaom3u4VNSVks3p3m6nBOa/XBDFKyi/D3cmNw2/qZpKwv7NVqLQB4e/E+Vu4/waJdaVjMJh66qI2LoxMREXG8GifV/vnPf/LII49w6aWX8txzz/Hcc89x2WWX8eijj/LUU08BsGDBAgYNGuTwYEVERERERBwtI78Em63+Vkw0BvO2HyOroJToQC8Gtglz2jp/HWC/8fvVuiRyikqdts75Yu7WYwD0aRFKSD1q/Vjhmp6xmEywfN9xDp/Id3U4LrWgvEptYOswvNwtLo7m9ML8Pbmhb/2vVvu+vHXh2E5R9frPs76Y0CuWUF8PkjIKuf3TdQD8pUcMLcL8XByZiIiI49U4qXbrrbeydOlSfH19mTVrFrNmzcLHx4elS5dyyy23APDQQw/x5ZdfOjxYERERERERR1q8K42e/1rIZe/83uhvyrvSzDX2SqPxPWIcupfa/xrYugmtw/3IKy7jq/L92+T0Klo/ju5Uv6rUKsSE+DCwtT0JO3NNw3w9dx/LZduR7HOaIzO/hB+32FsVXpQQ4YiwnOr2gS3x9bCw7UhOvWzdWVRq5afy9/7lav1YLT4ebtw60P6lhZyiMjzdzNw/vLWLoxIREXGOGiXVSktLufnmm4mOjmbmzJls2LCBDRs2MHPmTPr27eusGEVERERERBzOMAxeXbAbq81g65Fsxr65vPLGtNSdA+l5rDqQgckEf+kZ49S1TCYTt/S376027fdDlFltTl2vIUvKKGDrkWzMJupd68c/u/bCWAC+WZ9ESVnDej0TTxRwydvLufit5Tz01WYy8ktqPMfcrSlc9PpSDqTn4+fpxtB29b9VYYivB5P6xQPw+sK9dVIpnJZbxJuL9rI3NfesY5fsTiO3qIzoQC8ujHf8/o7nq+t7xxHs4w7ATf2aExHg5eKIREREnKNGSTV3d3e+/fZbZ8UiIiIiIiJSZ5bvO862Izl4uZvpHhdMXnEZ93y+kf/7bitFpVZXh9dofFleMTa4TRhNg7ydvt7l3ZoS4uvBkaxC5m2vf1Uy9UVFlVrvFqE08fN0cTSnN6x9OOH+nhzPK2H+jmOuDqdG3li0pzIR+O2GZIa/tpTvNiZXqyViWm4Rd85Yz12fbeB4XgltIvz47K+9CK6HbTpP5dYBLfD3dGNnSg7znVytlppTxDXvreK1BXu44r8rWLYn/Yzjv9tob/14ademmJ1YOXu+8fV0441rujGpbzz3DG3l6nBEREScpsbtHy+//HJmz57thFBERERERETqzpSl+wH7vkxf3tabu4e0xGSCz1cncvk7v7MvLc/FEZ7/SspsfLM+GYBryiuOnM3L3cJ1vexrTV1+oE7WrCtFpVauencFF722lDcX7SXxREGN5zAMg13Hcpi1wZ5YGN0pytFhOpS7xczV5RWOn69OdHE01bcvLZfZ5cmbf13RkXaR/mTkl/DAl5u54aM1p21HaxgG321MZsTry/h52zHczCbuG9aaOff2p0tMUB0+g3MT5OPBjX3jAXh/2X6nrZOWU8S176/iwPF8zCbIKy7j5ulrT9v+NbuglMW77Em3y7tFOy2u89WgNmE8fWkH/DzdXB2KiIiI09T4b7nWrVszefJkfv/9d7p3746vr2+V8/fdd5/DghMREREREXGGLclZ/L7vBG5mE38d0Bw3i5lHRrajV/NQHvxqE7uO5XLp28t57vKOXHlBM1eHe95auDOVE/klhPl71mnbuuv6xDFl6QE2JGax7lAGPc6TFm8/bD7KusOZALy2YA+vLdhDj7hgLu/WlIs7RxHkc+oqplKrjbUHM1iwM5WFO1NJyigEwGI2Maoet36scHXPGN5evI8V+09w8Hg+zZv4nv0iF3ttwR5sBozsEMHEXnH8pUcMH/x2gP8s3Mtve48z4vVl3D+8DX8d0Bx3i/370CnZhfzfrK0s3m1P+nSIDuCVq7qQEB3gyqdSazf2jef9Zfbfw/WHM+ge59jfw7ScIq75wJ5QaxrkzSe3XMhbi/Yye9NRHv12C0mZBTx4URtMpj+q0eZuS6HEaqNdpD/tIhvmn6uIiIg4l8moTl+BP2nevPnpJzOZOHCg4X3TLycnh8DAQLKzswkI0D+aRERERETOd3d9tp65W49xZbemvHZ11yrn0nKKuP/LTazYfwKAq7o3Y/JlHfDx0DfvHe36qav5be9x7h7SkkdGtqvTtf/+7Ra+WJvE8PbhfHhjzzpd21kufXs5W5KzGds5iuyCUn7ff5yK/8fvbjExpG04V17QlCHtwikus7F0dzoLdqSyuHwPqQqebmYGtG7C1T1juSghwkXPpmYmTVvDkt3p3D6wBY+Pae/qcM5o25FsLn5rOSYTzLt/IG0i/CvPHTqezz9mb+X3ffbPn3aR/rxwZSd2puTywtyd5BaX4WEx87fhrbltYIvKhFtD9dg3W/hyXRKjOkQy5fruDps3LddeobY/PZ/oQC++uK0PsaE+GIbBawv28Nav+wC4vGs0L13VGU83CwB/eW8law5m8PfR7bhjUEuHxSMiIq6l+//iSDVOqp2P9EslIiIiItJ4HEjPY9hrSzEM+w3ttpH+J42x2gze/nUf/1lkryZpGebLu9d1r3LzW85NUkYBA15eDMCyR4YQG+pTp+v/+X0w/4GBDf613ZSUxeXv/I6HxczKx4cS6ufJsewifth8hO82HmVnSk7lWH9PN4rKrJRa/7gdEOrrwbD24QxvH0H/1k0aXBJ5/vZj3PbpekJ8PVj5+NDKJEl9VJEAvLxrNG9c0+2k84ZhMGvDEZ77aQeZBaVVznWLDeKVqzrTKrxhv18r7E3N5aLXl2EyweKHBhPvgCrD9Nxirv1gFfvS8qok1P7sy7WJ/N9327DaDHq3COG963qQV1JGvxd/xWSC3x8bSnQd7PEoIiJ1Q/f/xZEa9leaREREREREauj9ZQcwDBjWLvyUCTWwt7372/DWfPbX3oT7e7I/PZ+JH64mu7D0lOOl5r4s39Oof6smdZ5QA2gR5lfZ2rBif72G7NOVhwG4uHMUoX6eAEQGenHbwJb8/LcB/HL/AG4f1ILIAC9yi8sotRq0DPPljkEt+fbOPqz5x3BevqoLIzpENriEGsDQduFEBniRkV/C9xuPujqc01p3KIMlu9OxmE3cP7zNKceYTCbGdW/GoocGc+UFTQHwcjfzz4sT+OaOvudNQg2gdYQ/Q9uFYxgwdfnBc57vzwm1qEAvZt7W+5SfL1f3jGXapJ74ebqx6kAG46asYMoS++dA7+ahSqiJiIjIadWqUi05OZkffviBxMRESkpKqpx77bXXHBZcXVGmWkRERESkcUjNKWLAS4spsdr45o4+1dpL60ReMeOnrOTA8Xwm9Irl+Ss61UGk57cyq42+L/5KWm4xb0/oxsWdo10Sx+akLC5753fczCaWPjqEpg30RnpGfgm9X1hESZmNWXf15YLY4NOOtdoMth7JJtDbvUHsPVYT7y/bz/Nzd9E0yJtfHx5U76rVDMPgmvdXsfpgBtdeGMMLV3au1nU7U3II9vEgMtDLyRG6xsr9J7j2g1V4uZtZ+fdhBPueeu+/s0nPLWbCB6vYm5ZHZIAXX97em7jQM7/HdxzN4ebpazmWU1R57KVxnbi6Z2ytYhARkfpJ9//FkWpcqbZo0SLatm3Lu+++y6uvvsrixYuZNm0aH330EZs2bXJCiCIiIiIiIo7x0fKDlFht9IwPrlZCDSDUz5Pnr7Qn0j5fncjaQxnODLFR+HVXGmm5xYT6ejAiIdJlcXSJCaJvy1DKbAYf/tbw9gev8PW6JErKbHRsGkC3mKAzjrWYTXSNCTrvEmoAN/SJJzLAiyNZhXy2KtEpa2QXlrL2UAafrjrMy7/sIjmzoNrXLt93nNUHM/CwmLl3aOtqX9c+KuC8TagB9G4RQsemARSV2pix6nCt5jieVzWh9sVtZ0+oASREB/Dd3X1pV1617OFmZlTHqFrFICIiIo1DjZNqjz/+OA8//DBbt27Fy8uLb7/9lqSkJAYNGsT48eOdEaOIiIiIiMg5yy4s5bPV9hvtdwxqWaNre7cI5eoeMQA8PmsrxWVWh8fXmHxR3vpxXPdmeLi5dleCOwfb3wtfrEkiM7/kLKPrH6vNYMZqeyLi+t5xmEwmF0fkOl7uFu4bZk9WvbN4H3nFZbWeq7jMyvaj2Xy3MZkXft7JpGlr6PPCIro8M5/xU1byz9nb+O+S/Vz93ipSsgvPOp9hGPx73m4AJvaOVXvBPzGZTNw6oAUAH688RFFpzT5fT/wpoRYR4MnM23rXaG+2qEBvvr6jD5P6xvPc5R0J9Hav0foiIiLSuNT4/73s3LmTG264AQA3NzcKCwvx8/Nj8uTJvPTSSw4PUERERERExBFmrDpMXnEZbSP8GdI2vMbXPz6mHU38PNiXlseUJQ23qsnVUrILWbI7DYCre8a4OBr7nm4dmwZQWGrl45WHXB1OjS3dk0ZSRiEBXm5c2qWpq8NxufE9mhEf6sOJ/BI+quUeXe8v20/Hp+Yx9s3lPPDlZt5beoAlu9NJyba3CIwO9GJI2zBiQrw5klXIdR+u5kRe8RnnnL8jlc3J2fh4WLhrcKtaxXU+G9MpiqZB3hzPK2H2xiPVvq6wxMrN09eyJ9WeUPvitj61qsL093Ln6Us78Jcerv9MEhERkfqtxkk1X1/fyn3UoqKi2L//jw2djx8/7rjIREREREREHKSo1Mq03+032G8f1AKzuebVPEE+Hjx5SQfAXgWzLy3PoTE2Fl+tTcZmwIXNQ2gZ5ufqcDCZTJWVi9NXHKKgpPbVTa7w6Up7ldr4HjF4e9SvPcRcwd1i5sERbQH4YNmBGlcfLtuTzvNzd1FqNQjwcuPC+BCu7x3Hc5d35Js7+rDl6RGseHwY0266kJm39iY60Iv96fnc8NEacopKTzmn1Wbw2vw9ANzUL54wf89ze5LnIXeLmZv6xQPw4fKD2GzGWa+x2Qwe+noTm5OzCfJx5/Nbe5+XbU1FRESkfql2Um3y5Mnk5+fTu3dvli9fDsCYMWN46KGH+Ne//sXNN99M7969nRaoiIiIiIhIbX2zPpnjeSU0DfLmki7RtZ7nks5RDG4bRonVxv/N2lqtG7/yB6vN4Mu19hacEy6MdXE0fxjdMYq4UB+yCkr5Yk2Sq8OptsQTBSzZkw7Adb3jXBxN/XFxpygSogLILS7j3aX7z35BubTcIh78ahMAE3rFsvmpEXx1Rx+evbwj1/WOo0d8CAFef7QGbBbsw6d/7UWorwfbj+Zwy/S1FJac3Lrwxy1H2Z2ai7+XG7cNqFnr2cbk6p4x+Hu6sS8tjyV70s46/tUFu5m79RjuFhPvXde9XiTpRURE5PxX7aTaM888Q35+Pq+99hq9evWqPDZs2DC+/PJL4uPjmTp1qtMCFRERERERqY0yq433l9nbNf51QHPcLbXfw8tkMvHsZR3xdrew5lAGX61rOAmY+mDZ3nSOZhcR6O3OqI6Rrg6nksVs4raB9j2dPvztAKVWm4sjqp7PVh/GMGBgmzBV6PyJ2WzikZH2arWPVxziWHnbxjOx2gwe+HITx/NKaBfpz5MXJ1Rrf7qWYX58csuF+Hu5sfZQJnfMWE9J2R/vn1KrjdcX2KvUbh/YgkAf7dd1Ov5e7kzoZU+2V3xmn84365N5Z7E9YfrilZ3p1SLU6fGJiIiIQA2SaoZh/wZmixYt6Ny5M2BvBTllyhS2bNnCt99+S1ycvhknIiIiIiL1y8/bjpGYUUCwj7tD9vCKCfHhoRFtAHh+7k7Scs9+w17svlhjr1K7oltTvNzrV6vCcRc0o4mfJ0ezi/hh01FXh3NWRaVWvixP6l6vKrWTDG4bRs/4YIrLbPxn0d6zjn93yT5+33cCb3cLb0+4oEbvzw7RgUy/qSfe7haW7knn/i83UlaemP12fTKHThQQ6uvBTf2a1/r5NBaT+sXjZjax6kAGW5OzTzlm9YETPD5rCwB3D2nJuO7N6jJEERERaeRq9BXN6nxLS0REREREpL4wDIN3l9irGSb1bY6Ph5tD5p3UN55OTQPJKSpj8pwdDpnzfJeWU8SinfaWbtfWo9aPFbzcLdzS3570eG/Z/nrf2vPHLSlkFZTSNMiboe3CXR1OvWMymXh0VDsAvlqXxP+3d9/hUVVbH8e/kx5SCWmEJHQIvXcQRYpKEUEURRQLKqJeLMhFBRW9KvYONkAFROmigMAF6b3X0AmQnpBeZ+a8f0Ry5VUIZSaThN/nefJA5rR1QhaT7HX22ieSsy+679aTqXzw52yyCbc3ok7wlbcRbFU9gK/ub4WbsxOL98Yzdt5e8gotxQW9ETfWxsvdNv//VGRV/Tzp92eL3q/X/n222snkbB6bvp1Ci8FtTUJ5rkf90g5RRERErnNXVFSrV68eAQEBl/wQEREREREpK9YcSeZAXAaers7c38F2s3lcnJ14a0ATnJ1M/LonjpWHEmx27opq9vYzmK0GLSP9qR/q4+hw/tGQ9pH4uLtwOCGLlYdKXtPJkX7YeBIoWvvL2UkPwP6TNjUC6BYVjMVqFBfN/r9z2QU8/eNOrEbRDMo7r2HWU5e6QXxyTwucTEXf73d8sYG49DxCfT205t0VeKRLUSvW3/bGceZcTvHr6TmFPDRtK2k5hTQL9+P9Qc1x0ve+iIiIlLIrekzqtddew8/Pz16xiIiIiIiI2NTkP2ep3dM2kspebjY9d+NqfjzcuSZfrTnOuAX7afdMlQo5E2Xf2XQycguJqupLwBV+DXMKzOw5k87OmDS+23ASgMFlcJbaeb4ergxpX53Jq48xafUxujcMcXRI/2j36TR2n0nHzdnJJi1NK7Lne9Zn5aFEFu2O5fGutWgU9r8xDcMwGD1nD3HpedQM9OL1/o2vuUPPLY1DeefOZjw/ezcH4zIAePrmumWu3WlZ1jDMl851All3NJmp608yrk9DCi1WRszYzvHkbML8PPj6gdZ4uulrKiIiIqXvin7jGzx4MMHBaishIiIiIiJl367TaWw8noKLk4lHuthnLaNR3euyeG8cZ87l8sHyw4zr09Au13GUXafTGPDFes53QgzxdScq1Jeoqj40CPWlQVVfagV54ershGEYHE/OZmdMGjtjzrEzJo3ohEwsf2mj6F/JlT5Nqzrobi7PQ51qMGXdCbafOsfWk6m0qWH/jiyGYbD5RCpbTqQyoGU1witXuuT+P2w6BcBtTUIJ9Ha3e3zlWcMwX/o1C+OX3bG893s0Ux9sW7xt6vqTrDiYgJuzE5/d2wJvGxXF72wVTna+mVd+2U+tQC8GtdaaX1dq+A21WHc0mVlbYnj65rq8tfggG46l4OXmzLfD2hDs4+HoEEVEROQ6ddk/MWo9NRERERERKU8+X3UUgH7Nwwjz97TLNSq5ufBG/8YMm7qVqetPcHvzMJqG+9vlWqXNMAwmLNqP1QAfDxcy88wkZOSTkJHE6sNJxfu5OpuoFehNfEYe6bmFfztPqK8HLSL9aRHpT/cGITZb185egn09GNgqnB+3xDDpj2O0GWa/olpSZj5zd5zhp63/W/PrqzXHebl3A+5uE/GPv4efyy5g0e5YAIZ2qGG32CqSZ3vUY/HeOFZFJxUXSveeSeetJQcBeKl3gwtmsNnCAx1r0CLSn1A/D1ydr2jlDQFuqBtI/RAfohMyuf/bzew+k46TCT69twUNqvo6OjwRERG5jl32bzOGUbYXaRYRERERETlvf2w6yw8kYDLBEzfWseu1bqwfzO3Nw1i4K5YX5+9l4cjOFWKNq192x7IjJo1Kbs6seLYrXu4uRMdncig+g0Nx//szM99MdEImAO4uTjQN96NFZGVaRPjTPNKfqn72KWja06M31GLW1hhWHkrkUHwGUaG2G8S3WA3WHkli1pbTrDiYgPnPmXxebs5Uq+zJ4YQs/j1vL0v3x/P2gKaE+l04I2f29tPkm600CvOlZaS/zeKqyGoEenFXmwhmbo7hnaWHmDKsDU/+uINCi0GvRiE2XW/xrypKgd0RTKaiGcaj5+xh95l0AF7u3ZBuUWWzJauIiIhcPy67qGa1Wu0Zh4iIiIiIiM18trJollqfpmHUCfa2+/XG9WnIqkOJ7DubwcwtMQxtb59B+tKSW2Dh7SWHAHjixtqE+BYVdlpVr0yr6pWL9zMMgzPncjmamEWgtztRVX0qxKycmoFe3Na4Kr/tjePL1cf58O7m13zO2LRcft52mtnbznA2Lbf49eYR/tzTNoI+TcPwcHVmyroTvLssmj+ik+j54Wpe7deIO1pUw2QyYbUaTN8UA8DQ9tXVUeYKPN2tLnO3n2HryXPc/eUmTqXkUM3fk3cGNtPXsYzq1zyMd3+PJjEzn/vaR/JgpxqODklEREQEh/62M2nSJJo2bYqvry++vr506NCBJUuWFG/Py8tj5MiRVKlSBW9vbwYOHEhCQsIF54iJiaF3795UqlSJ4OBgRo8ejdlsLu1bERERERGRMiI6PpMl++IxmeCpbvadpXZeoLc7z/eqD8C7Sw+RkpVfKte1ly/XHCMuPY9q/p480qXWRfczmUxEBFTipqhgmoT7VYiC2nmPd60NFM3YO5WSfU3n+nzVUTpPXMlHK45wNi0XP09XhnWswdJRXVgwshN3t4nEy90FZycTw2+oxeKnO9Ms3I+MPDPP/rybR3/YTlJmPquPJBGTmoOPhwu3N69mi9u8boT6eTCsYw0ADsRl4Oxk4pN7WuBXydWxgclFubs48+0DbXi9f2Ne7dtIxU8REREpExz6G094eDhvv/0227dvZ9u2bXTr1o3bb7+d/fv3A/DMM8+waNEiZs+ezerVq4mNjWXAgAHFx1ssFnr37k1BQQEbNmzgu+++Y9q0aYwfP95RtyQiIiIi1znDMNh4LIXkcl5UKc8+XXkEgNsaV6VeiE+pXXdIu+o0rOpLRp6ZiUsPldp1bS0uPZfJq48BMPa2KDxcnR0ckWM0Cfeja70gLFaDcQv3X/WSCNtPneO9ZdFYDWhfK4CPBzdn84s382q/RhdtK1kn2Ie5IzryfM96uDqbWH4ggZ4frmbin7MHB7WKwNPt+vx3uRaPd62Nj3tRw57ne9a/YNallE1Nwv0Y2r46LhWoYC8iIiLlm8koY4ulBQQE8O6773LnnXcSFBTEzJkzufPOOwE4dOgQDRo0YOPGjbRv354lS5bQp08fYmNjCQkp6qs9efJkxowZQ1JSEm5ubpd1zYyMDPz8/EhPT8fXVwveioiIiMjV+/S/R3h/+WFcnEz0aBjC4LaRdK4TWCHW2CoPjiZm0uPDNRgGLPlXFxpULd2f77efSmXgpI0AzB3RsVwO2o+atZMFu2JpU6MyPz/W4bqeHXIiOZteH62hwGzl48HNr3h2WF6hhT6fruNoYhYDW4bz/l3NrjiGA7EZPPvzLg7FZxa/tvK5rtQKsn9b04po28lUjiRmcXfrCJz0/7KIiMh1QeP/Yktl5lEfi8XCrFmzyM7OpkOHDmzfvp3CwkK6d+9evE9UVBSRkZFs3Fj0S+rGjRtp0qRJcUENoFevXmRkZBTPdvsn+fn5ZGRkXPAhIiIiInKtYlJy+GxV0VpeZqvBkn3xPDBlCze8s4qPVxwh9i/rKIl9fLbyKIYBvRqFlHpBDaBV9QAGtQoHYPzCfVisZeoZxhLtiDnHgl2xmEwwvo/ardUM9OJfN9cFYMKiA5zLLrii4z9deYSjiVkE+bgzrk+Dq4qhYZgvvzzZmae61cHZycTtzcNUULsGrWsEcE/bSBXUREREROSqOLyotnfvXry9vXF3d+fxxx9n/vz5NGzYkPj4eNzc3PD3979g/5CQEOLj4wGIj4+/oKB2fvv5bRfz1ltv4efnV/wRERFh25sSERERkevShF/3k2+20rF2FZb8qwvDOtbA18OFs2m5fLjiMJ0nruTBqVv4fX88hRaro8OtcI4nZfHL7lgAnupW12FxjLk1Cl8PF/bHZjBz8ymHxXGlrFaDCYsOAHBny3CahPs5OKKyYXiXWtQP8SElu4A3Fx+87OP2nU1n8urjALx+e2P8K11eJ5V/4ubixHM967P/tV68P+jKZ7uJiIiIiIhtOLyoVr9+fXbt2sXmzZsZMWIEDzzwAAcOHLDrNceOHUt6enrxx+nTp+16PRERERGp+FYcSGDFwURcnU1MuL0RDar68mq/Rmx5qTsf3d2cdjUDsBqwKjqJx37YTse3VzJ/5xlHh12hfL7qGFYDujcIpnE1xxWEAr3deb5XfQDe/T263Kyv98vuWHadTsPLzZnRf8YvRQWtNwc0wWSC2dvPsOFoconHFFqsvDBnDxarQe8mVbmlcahNYvFwddbaUiIiIiIiDuTwn8bd3NyoU6cOrVq14q233qJZs2Z8/PHHhIaGUlBQQFpa2gX7JyQkEBpa9AtJaGgoCQkJf9t+ftvFuLu74+vre8GHiIiIiMjVyiu08NqvRe3HH+5cizrBPsXbPFyd6d+iGj891oGVz3Xlsa61CPR2Iykznxfm7CH6L+skydU7lZLNgl1nAcfOUjtvSLvqNArzJSPPzMQlhxwdTolyCsy8/WecT9xUh2BfDwdHVLa0ql6Zoe2rA/Di/L3kFVouuf+Xq49xIC6DypVcebVfo9IIUURERERESoHDi2r/n9VqJT8/n1atWuHq6sp///vf4m3R0dHExMTQoUMHADp06MDevXtJTEws3mf58uX4+vrSsGHDUo9dRERERK5PX/xxjNOpuVT18+CpbnUuul+tIG/G3tqADf++me4Ngim0GLwwZ3e5W3erLPpi1TEsVoMb6wfRLMLf0eHg7GRiwu2NgaLZTdtPnXNwRJc2efVx4jPyCK/sycOdazo6nDJpdK/6hPp6cDIlh09XHrnofkcSMvnkv0VrK77StxFBPu6lFaKIiIiIiNiZQ4tqY8eOZc2aNZw8eZK9e/cyduxY/vjjD4YMGYKfnx8PP/wwzz77LKtWrWL79u08+OCDdOjQgfbt2wPQs2dPGjZsyNChQ9m9eze///47L7/8MiNHjsTdXb+4iIiIiIj9nUzOZvLqYwCM69MQL3eXEo9xc3HiP3c0wcfDhd1n0pmy7oS9w6zQTqfmMHdHUSvNsjBL7bxW1StzV+twAMYt2FfqxdN8s4WcAnOJ+51Ny+XLP7+HX7ytAR6uzvYOrVzy8XDltduLZp19ufo4h+Iz/raPxWowes4eCixWbo4K5vbmYaUdpoiIiIiI2JFDi2qJiYncf//91K9fn5tvvpmtW7fy+++/06NHDwA+/PBD+vTpw8CBA7nhhhsIDQ1l3rx5xcc7Ozvz66+/4uzsTIcOHbjvvvu4//77mTBhgqNuSURERESuI4Zh8Oqi/RSYrXSpG8itV7BuUoivBy/d1gCA95ZFczI5215hVniTVh/DbDXoUjeQVtUrOzqcC4y5JQpfDxcOxGUwY/Mpu17LYjXYdTqNz1cdZcg3m2j66jKavLqM+77ZzIzNpy66ttvEJYfIN1tpWyPgir6Hr0e9GoXSq1EIZqvBv+fu/VuhdOr6E+w6nYaPuwv/uaMJJpPJQZGKiIiIiIg9mAzDuO57zWRkZODn50d6errWVxMRERGRy7ZsfzyP/rAdV2cTS0fdQO0g7ys63jAM7vt2M+uPptCuZgA/Dm+Pk5MG4a9EbFouXd9dRaHFYPbjHWhTI8DRIf3NDxtPMm7hfnw8XFj1/I0Eetumq4ZhGBxLymL90RTWH01m0/EUMvIuPjPNyQRtawZwa+Oq3NI4lBBfD7afSmXgpI2YTLDoyc40ruZnk9gqsvj0PLp/sJqsfDMTbm/E/R1qAEWzVm/5eA15hVbeGtCEe9pGOjZQEREREQE0/i+2VXJvGhERERER+ZvcAguvLToAwKM31LrighqAyWTi7QFN6fnhGjafSGXmlhjua1/d1qFWaJNXH6PQYtChVpUyWVADuLdddWZtPc3+2AwmLjnEu4OaXdP5DMPgg+WH+XnbaRIyLpx95uPhQvtaVehUuwqd6gTi4uzE0n3xLNkXx54z6Ww6nsqm46m8umg/rSIrcy6nAIBBrcJVULtMoX4ejLmlPuMW7uedpdH0aBhCiI8HY+buIa/QSqc6VRjcJsLRYYqIiIiIiB1ophqqVIuIiIjIlXt/WTSfrjxKNX9Plj97A5Xcrv55tSnrTjDh1wN4u7uw7JkbCPP3tGGkFVd8eh43vLOKAouVH4e3p0PtKo4O6aJ2xJxjwBcbAJg7ogOtql99AfCX3bE8/eNOoGh9vjY1KtOxdiCd6gTSOMwXF+d/7vJ/OjWH3/fHs3hvHDti0opf93JzZtXoGwn28bjqmK43VqvBnZM3sCMmjZ4NQ7ihXhAvL9iHp6szy565gYiASo4OUURERET+pPF/sSUV1VBSiYiISPlXaLGy50wadUN88PVwdXQ4Fd6J5Gx6fbiGAouVyfe14pZrXIfKYjUY9OcA/U31g5gyrI3WYroMry3az9T1J2lbI4CfHmtf5r9mL8zZzc/bztCwqi+/PNnposWvS8krtHDz+6s5m5bLY11r8Uz3eni4Ol/xeeLSc/l9XzzrjqYwoGU1bmtS9YrPcb2Ljs+k9ydrMVsN3JydKLBYGd+nIQ91runo0ERERETkLzT+L7ak9o8iIiIi5ZTVarDlZCq/7I5l8d440nIKaRrux7wRHa9qsF4uj2EYjF+4jwKLla71gujVKOSaz+nsZOKdO5ty28frWBWdxIJdZ7mjRbgNoi2f8gotpGYXkFNgJqfAQk6Bhdw//8wuMJP7558zN8cA8PTNdct8QQ1gzC1RLN0Xz4G4DGZsjuGBjjWu+BzTNpzkbFouob4ejLr56gpqAFX9PBnWqSbDOqkAdLXqh/rwWNdafL7qGAUWK62qV76qf1MRERERESk/VFQTERERKUcMw2B/bAYLd53l1z1xxKXnXbB9z5l0pm04ySNdajkoworv9/3xrD2SjJuzE6/2a2SzYk6dYB/+1b0u7/4ezWuLDtC5ThBBPu42OXdZYhgGG4+lcDIlh+SsfJIy80nOOv9RQHJmPpn55ss+X8tIfzrVKbttH/+qirc7o2+JYtyCfby3LJreTasS6H35/8YpWfl8vvIoAKN71cfT7eoKamI7T3Wry4oDicSm5TJxYFOcncp+cVdERERERK6eimoiIiIi5cDxpCx+2R3LL7tiOZ6cXfy6j4cLtzYOpV+zasSk5vDi/L28v+wwvRqFak0fO8gpMDNh0QEAHu9ai5qBXjY9/6M31OK3PXEciMvg1V/28/mQljY9f1nw5uKDfL32RIn7uTqbqOTmQiU35z8/XPAs/rsznq4u+Hq6MLR99XIxS+28e9tG8tPWGPadzeDtJYd4b1Czyz724/8eITPfTKMwX+5oUc2OUcrl8nB1ZuGTncg3W/HzVOtdEREREZGKTkU1ERERkTLMMAw+XH6YT/6cnQLg7uJE9wYh9Gsexo31g3B3cS7ed+Gus2w+kcrLC/Yx7UGty2Vr7y87TGx6HuGVPRlxYx2bn9/V2Yl37mzK7Z+v57e9cfTdF3/N67WVJZuPp/DNuqKC2k31gwjx9SDQ251AbzeCfDwI9HYj0MedQG93fD1cKuT3r7OTiQm3N2bAFxuYs/0M97SNoFX1gBKPO5qYxYw/212+1LsBTpoRVWZ4uDpfdRtOEREREREpX1RUExERESmjLFaDcQv3Fa8b1bVeEP1bhNGjYSje7n//Mc5kMvHmgCbc+tFaVh9OYtGeOPo1CyvtsCuslYcS+PbPgtCE2xvZrfVe42p+PHZDLb744xjjFu6jQ60q+FUq/zNgsvPNjJ6zB8OAu1tHMPHOpo4OyWFaRlbm7tYR/LTtNOMW7OeXJzuVuA7i20sOYrEadG8QTMfagaUUqYiIiIiIiPyVVrAXERERKYPyzRae+nEHMzfHYDLBG/0b891DbbmjRfg/FtTOqx3kzZPdimZQTVi0n7ScgtIKuUKLT8/juZ93AzCsYw26RYXY9XpP31yX2kFeJGXmM/6XfRiGYdfrlYaJSw8Rk5pDmJ8HL/Vp4OhwHO6FW+rj5+nKgbiM4hloF7PhWDIrDibi7GTi37fqayciIiIiIuIoKqqJiIiIlDFZ+WYemraVxXvjcXU28dk9LbmvffXLPv7xrrWpG+xNclYBby4+aMdIrw8Wq8G/Zu3kXE4hjcJ8GXtblN2v6eHqzDt3NsPZycTCXbH8uOW03a9pTxuOJvP9xlMAvHNnM3w9yv/Mu2tVxdud53vVB+C9ZdEkZeb/435Wq8F/fivK4yHtIqkT7F1qMYqIiIiIiMiFVFQTERERKUNSsvK59+tNrD+aQiU3Z6YOa0vvplWv6BxuLk68NaAJAD9vO8OGY8n2CPW68cl/j7D5RCpebs58dm/L4jXs7K1V9cqM/rPo8uqi/ew7m14q17W1zLxCRs/ZAxQVhTrXVevC8+5tG0njar5k5pl5e8mhf9xn3s6z7I/NwMfdhX/dXLeUIxQREREREZG/UlFNREREpIw4m5bLoC83sudMOgFebvw4vP1VFyBa1whgSLtIAF6av4+8QostQ71ubDyWwqcrjwDwnzuaUDPQq1Sv/2iXWnRvEEyB2coTM3aQnltYqte3hTcXH+JsWi7hlT158Ta1LvwrZycTr9/eGIC5O86w7WTqBdtzCyy893s0ACO71aGKt3upxygiIiIiIiL/o6KaiIiISBlwJCGTgV9s4HhSNmF+Hvz8WAeaRfhf0znH3BpFsI87J5Kz+XzVUdsEeh1JycrnX7N2YjVgUKtw+reoVuoxODmZeH9Qc8IrexKTmsPo2bvL1fpqaw4n8eOWovXC3r2zGV6XWA/wetUisjKD20QAMG7hfswWa/G2r9ceJz4jj2r+ngzrWMNBEYqIiIiIiMh5KqqJiIiIONiOmHMM+nIj8Rl51An2Zu4THW2ybpKvhysTbm8EwKQ/jhEdn3nN57xeWK0Gz83eTWJmPnWCvXntz6+jI/hVcuWLIS1xc3Zi2YEEvll7wmGxXIn03ELGzC1q+zisYw061K7i4IjKrhduicLP05WDcRlM31S09lxiRh6TVx8DigrkHq6l03ZURERERERELk5FNREREflHVqvBvrPpahtoR4Zh8NueOIZ8vZm0nEKaR/gz+7EOVPXztNk1ejUKpUfDEMxWg7Hz9mC1lp9ZTo70zbrj/BGdhLuLE5/d24JKbo6dYdU03J9xfRsC8PbSQ2z9f20CS0tiZh6nUrIva983fj1AXHoeNapU4oVb6ts5svItwMuteP2895cdJikznw+WHyanwELzCH/6XuG6iiIiIiIiImIfKqqJiIjIBQzDYPXhJPp9vo4+n67jwalbVYixg9i0XIZ/v52RM3eQW2jhhnpBzBzejspebja9jslkYsLtjfByc2ZHTBoz/mzFJxe3M+Yc7ywtWsdqfN+GRIX6OjiiIve1i6RfszAsVoMnZ+4gOSu/1K6dnlvIG78eoNPbK+n67h/cNXkjS/bGXdCq8K9WHkpg9vYzmEzw3qBmDi9Klgf3tI2kSTU/MvPNPP3jTn7edhqAcX0aYDKZHBydiIiIiIiIgIpqIiIi8hfbTqZy91ebeGDKFvadzQBg4/EUvtt40rGBVSAWq8HU9Sfo8cFqVhxMwNXZxFPd6vDN/a3tVnio6ufJC7dEAfDOkkPEp+fZ5ToVQXpuIU/9uBOz1aB3k6rc2zbS0SEVM5lMvDWgCbWDvEjIyGfUrF1Y7FzwNlus/LDxJDe+u4pv1p2g0GLgZIItJ1MZMWMHXd/9g6/WHCM9t7D4mLScAv49dy8Aj3SuSesaAXaNsaJwdjLxev/GmExF/+9aDbitSSitquvrJyIiIiIiUlaYjPK00rmdZGRk4OfnR3p6Or6+ZeNJZBERkdK072w67y+LZlV0EgBuLk480KE6lb3ceGdpNB6uTiz91w3UCPRycKSlY/mBBN5achBnk4lKbs5UcnMp+tPdhUquzlRyd6aSmzOVK7nRtmYAjcL8cHYqeSbJgdgMxs7bw+4z6QC0ql6ZtwY0oV6Ij71vCYvVYOCkDew6nUa7mgF8/3Bb3F0qxhpNmXmFLNodR4tIfxpUvfqf5QzDYOTMHSzeG09EgCe/Pd0FXw9XG0ZqG4cTMrn9s/XkFlp4+ua6PNujnl2us/pwEm/8eoAjiVkA1An25uXeDYgK9WX6plPM3BJDanYBAJXcnBnYMpxhnWrw2cqjzN95llpBXix+uovWArtC/567h1lbT+PqbGLFs12pXuX6+H9XRERERMReNP4vtqSiGkoqERG5fh1PyuL95Yf5bU8cUDRT4q7WETx9cx2q+nliGAZDv93CuqPJtK5emZ8e63BZxaPyLDOvkJveW31FrfV8PVzoULsKneoE0rF2ILWDvC5o15ZbYOHj/x7h67XHsVgNfNxdGHNrFPe2jcSpFL+ehxMyuePz9WQXWLi9eRgf3tW8VK9vL6Nm7WTBrlgAokJ9uKNFNW5vXo1QP48SjzUMg/2xGSw/kMDyAwkciMvAxcnEnBEdaR7hb+fIr978nWd45qfdmEzw3YNtuaFekM3OfTQxkzd+O8gffxbZK1dy5Zke9bi3bSQuzv9rdJFXaGHhrrNMXX+SQ/GZF5zDyQRzR3SkRWRlm8V1vUjLKWDsvL10qRvEve3KzkxJEREREZHySuP/YksqqqGkEhGR68/ZtFw+WXGEOTvOYLEamEzQr1kYz3Sv97fZaGfO5XDLR2vJyjfzcu8GPNKlloOiLh0Tlx5i0h/HqBnoxX/6NyanwEJOoYWcfHPR3wvMZBdYyC2wcOZcDpuPp5KZb77gHCG+7nSqHUjHOoF4u7vwn8UHOJ2aC8CtjUN5tV8jQnxLLvjYw5rDSTw0bStmq8GIG2sz5s+2kOXVieRsbn7/D6wGuDk7UfDnGl8mE3SsXYX+zatxa5OqeLv/r7VmvtnCpuOprDiQwIqDCcT9pR2ms5OJ1/o14r721Uv9Xq7U2Hl7+XFLDJUrufLb010I8/e8pvOdyy7goxWHmb45BovVwNXZxAMdavBUt7r4Vbr4jD3DMNh4PIUp607y30MJGAYV4ntLREREREQqBo3/iy2pqIaSSkRErh/JWfl8seoY0zedKi4+dG8QzHM961+ybd6PW2IYO28v7i5OLP5XF2oHeZdWyKUqJiWH7h+spsBi5Zv7W9O9YUiJx5gtVvaeTWfDsRTWH01m26lzFJitf9uvqp8HE25vTI/LOKe9zd52mtFz9gDwev/GDC0HBaSLGT17N7O3n6FbVDAf3NWMxXvjWbDzLFtOphbv4+HqRI+GobStGcCmYymsPpxE1l8KoZ6uztxQL5AeDUO5qX4QVbzdHXErVyyv0MLASRvYH5tB8wh/Zj3a/qpbLR5NzGLwVxtJzipq59ijYQgv3taAmlfY8vVUSjbR8Znc3CCkws9qFRERERGR8kHj/2JLKqqhpBIRkYovPbeQb9Ye59t1J8gpsADQvlYAo3tF0ap6ye3ZDMPg/ilbWHskmRaR/sx5vGOFHDAfMX07S/bF07lOID883PaCFo6XK6/QwvZT51h/NJn1x1I4lZJN/+bVeL5X/QtmSznaxyuO8OGKwziZ4MuhrctEse9KnU7N4ab3/sBsNZj3REda/qXV4OnUHBbuOsu8nWc5npT9t2ODfdy5uUEIPRuG0KF2lXK77ldMSg59Pl1LRp6ZPk2r8sngFlfc0jMxM48BX2zgzLlc6gR7M6FfIzrWCbRTxCIiIiIiIqVL4/9iSyqqoaQSEZGKK7fAwrQNJ5m8+hjpuYUANAv3Y3SvKDrVqXJFRaPYtFx6fbiGzHwzY2+N4rGute0VtkNsOp7C4K824WSCJf+6gfqhPo4Oya4Mw+Dfc/fy07bTeLg68ePw9uVu/auX5u9lxuYYOtcJZPoj7f5xH8Mw2Hs2nfk7z3IgNoM2NQLo3jCEptX8KsR6cgAbjiVz/7dbMFsNnupWh+d61r/sY7PzzQz+ahN7z6ZTo0ol5o7oWG5m6omIiIiIiFwOjf+LLZWdx6VFRETEZgrMVn7aGsMnK4+SlJkPQN1gb57rWZ9ejUKuagZWmL8n4/o25IU5e3h/+WG6RQVTN6RiFJ4sVoMJiw4AcG+7yApfUAMwmUy8cUdj4jPyWH04iUe+28a8JzpSvcqVtftzlPj0PGZvOwPAU93qXHQ/k8lE03B/mob7l1Jkpa9j7UDeHNCEF+bs4dOVR6lRxYuBrcJLPM5ssfLkzB3sPZtOgJcb0x5sq4KaiIiIiIiIyCU4OToAERERsa1Nx1O4+YM/GLdwP0mZ+UQEePLBXc1YOuoGbmkcelUFtfMGtQrnpvpBFJitPD97N2bL39cOK49mbzvNgbgMfDxceLbH5c/yKe9cnZ34YkhLGlfzJSW7gAembCElK9/RYV2WL9cco8BipW2NANrVquLocBzurtYRjLixaPbov+ftYfPxlEvubxgG4xbuY1V0Eh6uTnz7QGtqXOH6aSIiIiIiIiLXGxXVREREKpBz2QWMnLGD06m5BPu483r/xvz32RsZ0DLcJmugmUwm3hrQFB8PF3afSefLNcdtELVjZeYV8t6yaAD+dXNdArzcHBxR6fJyd2HKsDZU8/fkZEoOj3y/jdw/190rq5Iy85m5OQaAp26++Cy1683onvW5rUkohRaDx6Zv50Ty39eSO+/zVUf5cctpnEzwyeAW5a71p4iIiIiIiIgjqKgmIiJSgby5+CAp2QXUD/Hhj9E3MrR9ddxcbPt2H+rnwat9GwHw8YojRMdn2vT8pe2zVUdJziqgVqAX93eo4ehwHCLYx4PvHmqDn6crO2PS+NesnVisZXfZ3W/WHiffbKV5hD+d6wQ6Opwyw8nJxAd3NadZhD9pOYU8NG0r57IL/rbf3O1neG/ZYQBe7deIno1CSztUERERERERkXJJRTUREZEKYsPRZGZvP4PJBG8OaEIlN/stnTqgZTW6NwimwFLUBrKwnLaBPJWSzdR1JwF4qXcDmxcgy5M6wT58fX9r3FycWHYggTcXH3R0SP/oXHYBP2w6BRStpXYt7UwrIg9XZ76+vxXV/D05kZzNY9O3U2D+X36uPZLEmLl7AHisa63rtpAsIiIiIiIicjWu35EjERGRCiSv0MKL8/cCMLR9dVpVt28rN5PJxJt3NMHP05W9Z9MZO28v+eay3TLwn7y5+CAFFitd6gbSLSrY0eE4XNuaAXx4V3MAvl13gv8eTHBsQP9gyvoT5BRYaBTmq3+ziwj28eDbYa3xdndhy4lUxs7bi2EYHIjNYMT0HZitBn2bhTGmV5SjQxUREREREREpVxxaVHvrrbdo06YNPj4+BAcH079/f6Kjoy/YJy8vj5EjR1KlShW8vb0ZOHAgCQkXDvDExMTQu3dvKlWqRHBwMKNHj8ZsNpfmrYiIiDjUZyuPcjIlh1BfD0b3ql8q1wz29eDNO5pgMsGc7We468tNxKbllsq1bWHDsWR+35+As5OJcX0aasbTn3o3rcrDnWsCMHrOHhIz8+x6PYvVwDAur9Vkem4h09afBDRLrSRRob58dm8LnEwwd8cZ3vjtIA9O20JWvpl2NQN4b1BTnGywzqKIiIiIiIjI9cShRbXVq1czcuRINm3axPLlyyksLKRnz55kZ/9vUfVnnnmGRYsWMXv2bFavXk1sbCwDBgwo3m6xWOjduzcFBQVs2LCB7777jmnTpjF+/HhH3JKIiEipi47PZPLqYwC8dnsjfDxcS+3avZtWZdqDbfHzdGX36TT6frqODceSS+36V8tiNZiw6AAAQ9pFUi/Ex8ERlS2je9UnKtSH1OwCnp+9B+s1rK9mGAbnsgvYcyaNX/fE8sUfRxk7by/3fbOZru+uov7LS7j5/dXsOp1W4rm+33CSzHwz9UK86dlQ64CV5Mb6wbzWr2j9w2/XnSAhI5+6wd58NbQ17i7ODo5OREREREREpPwxGZf7aHApSEpKIjg4mNWrV3PDDTeQnp5OUFAQM2fO5M477wTg0KFDNGjQgI0bN9K+fXuWLFlCnz59iI2NJSQkBIDJkyczZswYkpKScHNzK/G6GRkZ+Pn5kZ6ejq+vr13vUUSkIsstsGBw6bcVV2cnXJ3VfdhWrFaDgZM3sDMmjV6NQvhyaGuHxHE6NYfHftjOgbgMnEzw71ujGN6lVpmdSTRzcwwvzt+Lr4cLq0ffRGWvkn9euN4cScikz6fryDdbGdenYfHstcsVm5bLmLl72BWTRmZ+yR0EXJ1NjO/biPvaRf7j901WvpnOE1eSllPIx4Obc3vzalcUz/XstUX7mbr+JME+7swf2Ylq/p6ODklERERERKTUaPxfbMnF0QH8VXp6OgABAQEAbN++ncLCQrp37168T1RUFJGRkcVFtY0bN9KkSZPighpAr169GDFiBPv376dFixZ/u05+fj75+fnFn2dkZNjrlkRErgtpOQU8MWMHG46llLivh6sTT3Wry2M31MJFxbVrNmPzKXbGpOHt7sJr/Ro7LI6IgErMe6IjL83fx9wdZ3hz8SF2n05n4p1N8XYvUz9ukJFXyPvLitpNj+peTwW1i6gb4sPLvRswbuF+Ji45RIdaVWgYdnm/fBxNzOL+bzcTm/6/1pEhvu5EVK5EZEAlwgOK/oyo7EmwrwcTlxxi6f54xi3Yx85T5/jPHU3wdLtwJtWMTadIyymkZqAXfZqG2fReK7qXezekU+1AmoT7EeLr4ehwRERERERERMqtMjPKZbVaGTVqFJ06daJx46JBwfj4eNzc3PD3979g35CQEOLj44v3+WtB7fz289v+yVtvvcVrr71m4zsQEbk+xaXncv+3WziSmHVZ++cVWnn392iWHUjg/UHNqBPsbecIK6649FwmLi0qDo25pT6hfo4dLPdwdea9QU1pHunPhEX7+W1vHNEJmXw5tBW1g2zz71xosZKclU9KVgHJWfkkZxWQkpVf/FrSn3/mmS0XPUd2vpmU7AJqBXkxtEN1m8RVUd3XvjqrDyex4mAi/5q1k0VPdcbD9dJtA3efTmPY1C2cyymkVqAXn9zTgjrB3pc8btJ9Lfl67XEmLo1m3s6zHIjLYNJ9ragZ6AUUzYL9eu1xAJ64sTbOWgvsijg7mejeMKTkHUVERERERETkkspMUW3kyJHs27ePdevW2f1aY8eO5dlnny3+PCMjg4iICLtfV0SkojmWlMX9327hbFouob4eTBnWpngQ/GJ+2xvHa4v2s/t0Gr0/WcvoXvV5qFNNnCrwILlhGKyKTmR1dBKDWkfQuJqfTc77ysL9ZOWbaRnpz5B2ZaM4ZDKZGNq+Og2r+vLEjO0cTczi9s/W896gZtzS+NrWwNp8PIWRM3eQnFVgk1jH92moVqQlMJlMTBzYlFs+XsuRxCzeXHyQCbdffEbkuiPJPPrDNnIKLDQN92PqsDZU8Xa/rOs8ekNtmob78+TMnRyKz6Tfp+t498/vmx+3xJCcVUB4ZU/6t1DbRxERERERERFxjDJRVHvyySf59ddfWbNmDeHh4cWvh4aGUlBQQFpa2gWz1RISEggNDS3eZ8uWLRecLyEhoXjbP3F3d8fdveQBHhERubg9Z9IYNnUrqdkF1Ar04vuH2xJeuVKJx93ZKpxOdarwwpw9rD2SzBu/HWTZ/gTeHdSU6lUuXZArjw7FZ/DGrwdZdzQZgO82nqJ/8zCe61mfiICSv14Xs3RfPMsOJODiZOKtAU3LXFGyVfXKLHqqM0/O3MmWE6k8Pn07T3WrwzPd611VrGsOJ/HoD9vIK7Ti7GSiipcbgd7uVPEu+jPQ240q3u5Fr/u4U6mE2VRVvN2oE+xztbd3Xani7c77g5px/5QtfL/xFF3rBXFzg7/Pelq8N45/zdpJocWgU50qfDm09RW3/mxfqwqLn/7z++Zk0ffN8C41+WV3LABP3FhHhVARERERERERcRiTYRiGoy5uGAZPPfUU8+fP548//qBu3boXbE9PTycoKIgff/yRgQMHAhAdHU1UVFTxmmpLliyhT58+xMXFERwcDMBXX33F6NGjSUxMvKzimRYqFBG5MuuPJvPo99vILrDQpJof0x68vNkof2UYBjO3xPCf3w6SU2ChkpszY29rwH3tIjGZylaB6GokZ+XzwfLDzNoSg9UAN2cnWteoXLzunJuzE/e1r86T3eoQcIVremXkFdLjg9UkZOTz5E11eL5XfXvcgk0UWqy8tfgQU9afAKBP06q8N6hZiS0E/2rFgQSemLGDAouVm+oHMem+Vld0vNjG678e4Nt1JwjwcmPpqC4E+/yv3eiMzad4ecE+DANuaxLKh3c3x93l6v+NCi1W3ll6iK/Xnih+raqfB3+MvvGazisiIiIiIiLXH43/iy05tKj2xBNPMHPmTBYuXEj9+v8bEPTz88PT0xOAESNGsHjxYqZNm4avry9PPfUUABs2bADAYrHQvHlzwsLCeOedd4iPj2fo0KE88sgjvPnmm5cVh5JKROTyLd4bx6hZuyiwWOlYuwpf3X/ls1H+KiYlh9FzdrP5RCoAXeoGMnFgU8L8PW0VcqnKN1uYtv4kn608Sma+GSgqMvz7lgZEVqnE3jPpvL30IOuPFhXXfNxdePzG2jzUqSaebpdXLBi3YB8/bDpFzUAvlvyrS7koMM3edpqx8/Zithq0ql6Zr4a2uqxC7K97Yhk1axdmq8GtjUP5eHAL3Fw0U8kR8s0W+n++gYNxGdxQL4hpw9pgMsFnK4/y/vLDANzbLpLXb29sszXPFu+N44U5e8jKN/Nav0Y80LGGTc4rIiIiIiIi1w+N/4stObSodrGZCFOnTmXYsGEA5OXl8dxzz/Hjjz+Sn59Pr169+OKLLy5o7Xjq1ClGjBjBH3/8gZeXFw888ABvv/02Li6XN8irpBIRuTwzN8fw0oK9GAbc2jiUjwZf22yU86xWg2kbTjJx6SHyzVZ8PFz45v7WtKtVxQZRlw7DMFi6L563lhwiJjUHgMbVfBnXu+Hf7sMwDNYeSebtJYc4EJcBQIivO890r8edrcJxcXYir9BCSnYByZn5pGTnk5xVQHJWPgnpeXy/6RSGATOHt6Nj7cBSv9erteFoMo9P305GnpnIgEpMfbANtYO8L7r/3O1nGD1nN1YD+jcP471BzXBR6z+HOpKQSZ9P15FvtvJy7wacOZfLtA0nAXiqWx2e7VHP5jNNz5zL4UBsBj0ahlSIWawiIiIiIiJSujT+L7bk0KJaWaGkEhG5NMMw+HzVUd5bVjQb5Z62kbzR33azUc47npTFMz/vZvfpNDxcnfj6/tZ0qRtk02vYmmEY7Ig5x8Sl0Wz5c7ZdsI87o3vVZ2DL8EuuH2a1GvyyO5b3lkVz5lwuAAFebhSarcWz3C7mzlbhvDeome1upJQcTcziwWlbOJ2ai5+nK5Pva0WH2n8vns7YfIqX5u8DYHCbCP5zRxObf7/J1flh0ynGLdh3wWuv9G3Ig51qOigiERERERERkYvT+L/YkopqKKlEpHyas/0MaTkFDO1Q3W5rDOUVWth28hzzdpxh3s6zADx5Ux2e62n72Sh/vebj07fzR3QSbs5OfDGkJd0bhtjlWtcir9DCL7ti+X7TSfadLZpt5u7ixGM31OKxrrXxuoKWmPlmC9M3xfDZyiOcyyksft3V2USgtztVvN2K/vRyJ9DbjRqBXgxsGV5u2yCmZOUz/Ptt7IhJw9XZxFsDmnJnq/Di7d+uO8Hrvx4AYFjHGozv0/CSxUkpXYZhMPz7baw4mIiLk4n3BjWjf4tqjg5LRERERERE5B9p/F9sSUU1lFQiUv78vO00L8zZA0C9EG/eH9ScJuF+13xewzA4GJfJuqNJrD2SzJYTqeSbrcXbx/dpyEOd7T8bJd9s4ekfd/L7/gRcnEx8PLgFvZtWtft1L0dMSg7TN5/i522nSfuzAObm4kT/5mH8q3s9ql3DWnBZ+WYOJ2Ti7+lKFW93fD1cKmy7u7xCC8/P3s2ve+KA/7UO/OKPY7z7ezQAj3etzZhb6lfYr0F5lp5TyOQ1x+haL4j25ahNq4iIiIiIiFx/NP4vtqSiGkoqESlftp86xz1fbaLAYsXD1Ym8QivOTiaevKkOT3arg+sVrjmVnJXPH9FJrDuSxLqjKSRn5V+wPcTXnc51grijRTU61y299bsKLVae+3k3v+yOxckE7w1qxoCW4SUfaAdWq8GaI0l8v/EUq6ITOf/OGV7Zk/vaV+eu1hEEeLk5JLbyzGo1eH95NJ+vOgZAg6q+HPxzjblnutfj6ZvrqKAmIiIiIiIiItdE4/9iSyqqoaQSKaui4zPZfCKlxP38PF3p1SgUD1f7tEAsS+LSc+n76XqSs/K5tXEor/dvzPiF+1i8Nx6ARmG+vH9XM6JCL/1/mcVqsPZIErO2nGbFwQTM1v+9FXi6OtO+VgCd6wZxQ91A6gR7O6ywYbEajJ23h5+3ncFkgv/0b8K97SLtfl2r1eBYUhY7Y9LYefoc64+mEJOaU7y9S91AHuhQg5uigrXOlw38vPU0L87fW/x9+OJtUTx6Q20HRyUiIiIiIiIiFYHG/8WWVFRDSSVSFv26J5ZnftpFoeXy/ouq5u/Jcz3r0b95tQq79lJeoYVBkzey92w6UaE+zB3RES93FwzDYNGeOMYv3EdaTiGuziZGda/HYzfUwuX/zVqLTcvl522nmb3tDGfTcotfbxTmy431g+hcJ4iW1f3ttkbb1bBaDV5dtJ/vN54C7NOCMjW7gF2nzxUV0WLS2H06jcx88wX7+Li7cGfrcIa2r06tIG+bXl9gw9FkPlh+mEGtw7m7jf0LpyIiIiIiIiJyfdD4v9iSimooqUTKmh82nWL8wn0YBrSM9Keq36XXqNp2KpWEjKKWhQ2q+vLvW6O4oW5ghWobZxgG/5q1i192xxLg5cbCkZ2ICKh0wT6JmXm8OG8vKw4mAtA8wp/372pGZEAl/nswkZ+2xrD6cBLnJ6X5ebpyR4tqDG4bUeLMNkczDIO3lxziyzXHARjdqz4jb6pzzeddvDeO936P5nhy9t+2ebo60zTcjxaRlWke4U+XuoF4ubtc8zVFRERERERERKT0aPxfbElFNZRUImWFYRh88t+jfLjiMABD2kUy4fbGJbbXyy2wMHXDCSb9cYzMvKLZRR1rV2HsrQ1oEu5n97hLw6Q/jjFx6SFcnExMf6Qd7WtV+cf9DMNg7o6zvPbLfjLzzbi7OOHj4XrBOmntawVwT9vIctcy0zAMPlpxhI//ewSAp7rV4dke9a6qeGoYBl+uOc7bSw4Vv1YryIsWEZVpEelPi0h/6of4/G2mn4iIiIiIiIiIlC8a/xdbUlENJZVIWWC1Gry2aD/f/dni7+mb6/JM97pXVDA5l13A56uO8v3GUxRYrAD0bRbG8z3rUb2Kl13iLg0rDyXw8HfbMAx4vX9jhravXuIxsWm5jJm7h7VHkgEI9Hbnzlbh3N0mgpqB5fdrAf8rMAL0aVqV/9zRBD9P18s+3myx8uqi/UzfFAPAsI41GNW9Lv6V3OwSr4iIiIiIiIiIOI7G/8WWVFRDSSXiaAVmK8/P3s0vu2MBeLVvQ4Z1uvo1s06n5vDB8sMs2HUWwwBXZxP3d6jBv2+NwrWczTw6mphJ/883kJVv5t52kbx5R5PLPtYwDJbsi8fFycRNUcHl7t4v5YeNJ3l10QEsVoNq/p58ck9zWlUPKPG4nAIzT83cyX8PJWIywcu9G/KwjddnExERERERERGRskPj/2JLKqqhpBJxpJwCM49P38Gaw0m4OJl4/65m3N68mk3OvT82nYlLo1lzOAmAAS2q8d6gZjiV0E6yrEjPKeT2z9dxMiWHtjUDmP5wO9xcKk5h7FrtjDnH07N2cjo1F2cnE/+6uS4jb6pz0XahiZl5PDxtG3vPpuPu4sTHg5tzS+OqpRy1iIiIiIiIiIiUJo3/iy2pqIaSSsRR0nIKeHDaVnbGpOHp6syk+1pyY/1gm19n8d44nvpxJxarwSOda/JS7wZXtQ5XaTJbrDw4bStrjyRTzd+TX57sRBVvd0eHVeZk5hUybsE+FuwqmuXYtkYAHw5uTjV/zwv2O5qYyQNTtnI2LZcALze+vr81rapXdkTIIiIiIiIiIiJSijT+L7akKQ8i4hBx6bkMmryRnTFp+Hm6Mv2RdnYpqAHc1qQq7wxsCsA3604wefVxu1zHVuLT83jll/2sPZKMp6szX9/fWgW1i/DxcOWjwS344K5meLk5s+VkKrd+tIYle+OK99l0PIUBX2zgbFouNapUYt6IjiqoiYiIiIiIiIiIyBXTTDVUqb5eFJit/LQ1hgNxmSXuW8XLjaEdqhPi61EKkV1/YtOKCmpn03IJ9fXg+4fbUi/Ex+7X/Wbtcd747SAAEwc24e42kXa/ZkkMwyAmNYfNJ1LZ8udHTGpO8fZJQ1pyaxO1KLwcJ5Oz+desnew+kw7APW0jaBFZmZfn76PAYqVlpD/fPNCGAC83B0cqIiIiIiIiIiKlReP/YksqqqGkqugMw2DZgQTeWnyQkyk5JR/wJ09XZx7uXJPHutbCx8PVjhFeX1KzCxg0eQPHkrKpUaUS0x9pR3jlSqV2/beXHGLy6mM4mWDSfa3o1Si01K4NRW0dD8VnsjPmHFtOnmPLiRQSMvIv2MfJBI3C/BjaoTp3tY4o1fjKuwKzlQ+WH+bLNcf467vbrY1D+fDu5ni4OjsuOBERERERERERKXUa/xdbUlENJVVFdiA2g9d/PcDG4ykABHq7M7hNBO4uF+98agB/RCeyIyYNgAAvN57qVoch7arjdonjpGRZ+Wbu/XoTe86kU9XPgzkjOv5t7St7MwyDMXP38PO2M7i5OPH9Q21pX6uK3a6XmJHHjpg0dp4+x86YNPaeSSe30HLBPq7OJpqF+9O2ZgBtawbQqnplFXKv0fqjyTzz0y4SM/N5pHNNXrytAU5OZXsdPRERERERERERsT2N/4stqaiGkqoiSszM44Nlh/lp22kMA9xcnBjepSYjbqyDt7tLiccbhsHv+xN45/dDHE/KBiAiwJPne9anb9OwCjk4n5iZx0crjpCSlU+HWlXoUi+IWoFemEy2udd8s4UHp25lw7EUKldyZfbjHakT7G2Tc18ps8XKiBk7WH4gAR93F358tD2Nq/nZ5NzpuYXM2X6GHTHn2BWTxtm03L/t4+PuQrMIf9rUKCqitYj01wwqO8jMK+RsWi5Rofp/XURERERERETkeqXxf7ElFdVQUlUkeYUWpqw/wecrj5JdUDQbqE/Tqoy5JYqIgCtvMWi2WPl52xk+XHGYpMyiFn2Nq/ny71sa0LluoE1jdxTDMJi34ywTfj1Aem7hBdvC/DzoXDeQLnWD6FQn8KrXorJYDUbO2MHS/fF4uTkzc3h7mkX42yD6q5dXaOH+KVvYciKVQG835jzekRqBXtd0zsy8Qu76chMH4zKKX3MyQb0QH1pEVqZFpD8tI/2pFehdIQuzIiIiIiIiIiIiZY3G/8WWVFRDSVURGIbBkn3xvLn4IGfOFc0Mahbhz/g+DWhVPeCaz59TYGbKuhNMXn2crHwzADfWD2LiwKaE+Hpc8/kd5WxaLi/O28vqw0kANKnmR69GIWw8nsLWE+cosFiL9zWZoFGYL13qBnFr41Cahvtf1jUMw+Dfc/fy07bTuDk7MfXBNnSqUzYKkhl5hdz9ZxEsIsCTuY93JPgq/z0LLVYemraVtUeSCfR248FONWkR6U/TcP/Lmh0pIiIiIiIiIiIitqfxf7ElFdVQUtlTem4hmXmFVPXzxNlOM3NOp+YwbuE+/oguKgyF+now5tb63N6sms1nA6Vk5fPZqqNM33SKQotBFS83Pri7OV3rBdn0OvZmtRr8uDWGtxYfIivfjJuLE890r8fwLjVxcS5aNy63wMKWk6msPZzEuqPJHIrPvOAcLSP9eahzTW5pFFp8zD95e8khJq8+hpMJvhjSklsaV7XrvV2pxMw8Bk3eyKmUHKJCffhxeHsqX+GMPMMweH72HubuOIOnqzM/Pdb+souOIiIiIiIiIiIiYj8a/xdbUlENJZU95JstfLn6OJ+vOkq+2Yqrs4nwypWIDKhE9SpFfxb93YuIAE8quV35TB6zxcq0DSd5f9lhcgstuDk78fiNtXm8a62rOt+VOJaUxZMzdxa3+RtxY22e7VEP10sUl8qKUynZjJm7h03HUwFoVb0yEwc2LXF9s8SMPNYdTWZVdBJL98VRaCn6ryPMz4P7O9ZgcJsI/CtdWIz6cvUx3lpyCICJA5twd5tIO9zRtYtJyWHg5A0kZeZTM9CLqcPaXFEryA+WH+aT/x7B2cnEN/e35qaoYDtGKyIiIiIiIiIiIpdL4/9iSyqqoaSytXVHkhm3cB8nkrMBcHYyYbFe+tusYVVf+jUPo2+zMKr5e5Z4jb1n0hk7fw/7zhYVtdrVDODNAU2oHXTpwpAt5RVa+M9vB/lh0ymgqDj1yT0tLit+R7BYDaauP8F7y6LJK7Ti6erMC7fU5/4ONa54FmFiZh7TN8UwY9MpUrILAPBwdWJgy3Ae7FSDOsE+/Lz1NC/M3QPA2FujeKxrbZvfky0dSchk2NStnE3LpXIlV766vzVtapTcOnTWlhj+PW8vAG8NaMI9bctm4VBEREREREREROR6pPF/sSUV1VBS2UpCRh6v/3qAX/fEARDs4864Pg25rUlV4jPyOJWSTUxKDqdSc4hJzSn6e0o2GXnmC87TpkZl+jWvxm2NQ6ni7X7Btux8Mx8sP8zU9SewGuDn6cpLtzVgUOtwTCb7tJcsyeK9cYyZs4fMfDN+nq68N6gZPRqGOCSWi9l+KpU3fjvIzpg0ADrUqsLEgU2JrFLpms6bV2hh0e5Ypqw/WTxrD6BtzQC2nUzFasBjXWsx9tYG13Sd0pKYmcfw77ax+0w6bs5OvDuoKbc3r3bR/VdFJ/LId9uwWA2e6laH53rWL8VoRUREREREREREpCQa/xdbUlENJdW1MlusfL/xFB8sP0xWvhknE9zfoQbP9qyHr4driccnZeaz7EA8v+yKZcvJVM5/Rzo7mehSN5B+zcLo2SiUrSdSeXnBPs6m5QLQr1kY4/o0JMjH/RJnLx0xKTk89eMOdp9JB+DhzjUZc0sUbi6ObQcZHZ/Ju79Hs+JgAgDe7i68eFsD7mkbYdMipGEYbDqeypT1J1hxMKH43/Du1hG8PbCJwwqeVyO3wMIzP+1i6f54AJ7pXo+nb67zt3vYdzadu77cSE6BhQEtq/H+oGbl6j5FRERERERERESuBxr/F1tSUQ0l1bXYGXOOlxfsY39s0Syl5hH+vNG/MY2r+V3V+eLSc/l1dxy/7I5l79n04tfdnJ0osFgBCK/syRv9G3Nj/bK1blWB2co7Sw/xzboTADQN9+Oze1pe82ywq3E6NYcPVxxm/s6zGAY4meCu1hGM6l6PUD8Pu177VEo2MzfH4OrsxKjudXEpB+vM/X9Wq8HEpYf4cs1xAAa0qMZbA5vg7uIMFH197/hiA8lZ+XSuE8iUYW0cXkAVERERERERERGRv9P4v9iSimooqa5UocXK1pOpLNh5ltnbz2D82YZxzC1RDG4TgdMVrs91MceTsvhldyy/7IrleHI2TqaiGWDP9KhHJTcXm1zDHlYcSOC52btJzy3Ex92FV/o1YmDLalc9i2nryVRWHUok0NudyIBKRARUIiLA8x+/BslZ+Xy+6igzNsUUFyFvaxLKsz3qUye49Nabqyhmbo5h3MJ9WKwG7WoG8OXQVgAMnLSBY0nZRIX6MPvxDvhcxoxMERERERERERERKX0a/xdbUlENJdXlSMzI44/oJFZFJ7L2SDJZ+f9bB+3OVuH8+9YoAr3t04bRMAyOJGbh5e5CNX9Pu1zD1s6m5fKvH3ey7dQ5ALo3CObNAU0I9rn8WWLJWfm8ufgg83ac/cftgd5uRQW2ypWIDKhEodXK9I2nyC6wANC5TiCje9WnWYT/Nd/P9WzN4SSemLGDrHwztQK9qOzlxvZT56jq58H8JzrZfeafiIiIiIiIiIiIXD2N/4stqaiGkuqfWKwGu06n8Ud0IquiE9l3NuOC7VW83OhaP4h72kbSpkaAg6Is28wWK1+tPc6Hyw9TaDHwr+TK67c3pm+zsEseZ7UazNwSwztLD5GRZ8ZkgtsaV8VqGJw+l0NMSg4ZeeaLHt803I8XekXRuW6grW/puhUdn8lD07YWr+fn4+HCnMc7Uj/Ux8GRiYiIiIiIiIiIyKVo/F9sSUU1lFT/5PEftrN0f/wFrzUL9+OmqGBuqh9Mk2p+NmvzWNEdis/guZ93F68717tpVV6/vTEBXm5/23ff2XReWrCP3afTAGhY1Zf/3NGYFpGVL9gvPaeQ0+dyOJ2aQ0xqDqfP5XAup5DbGlfltiahV91qUi4uMTOPx3/YzuGELL4a2oqOdVS0FBERERERERERKes0/i+2pKIaSqp/MmXdCT5acZgb6gVxU/1gbqgXRJCPfdo7Xg8KLVY+W3mUz1YdxWI1CPR24807mtCzUSgAGXmFfLDsMN9vPInVAG93F57rWY+h7avj4uzk4OjlPMMwyDdb8XB1dnQoIiIiIiIiIiIichk0/i+25NDR+jVr1tC3b1/CwsIwmUwsWLDggu2GYTB+/HiqVq2Kp6cn3bt358iRIxfsk5qaypAhQ/D19cXf35+HH36YrKysUryLiunedpHsGNeDz+5tycBW4SqoXSNXZyee6VGPBU90ol6IN8lZBTz6w3ae/WkXc7ef4eb3VzNtQ1FBrW+zMFY+15UHO9VUQa2MMZlMKqiJiIiIiIiIiIiIXKccOmKfnZ1Ns2bN+Pzzz/9x+zvvvMMnn3zC5MmT2bx5M15eXvTq1Yu8vLzifYYMGcL+/ftZvnw5v/76K2vWrOHRRx8trVuosDxcnVXQsYMm4X788mRnHutaCycTzNt5ludm7yYpM59agV5Mf7gdn97TgmBfD0eHKiIiIiIiIiIiIiIif1Fm2j+aTCbmz59P//79gaJZamFhYTz33HM8//zzAKSnpxMSEsK0adMYPHgwBw8epGHDhmzdupXWrVsDsHTpUm677TbOnDlDWFjYZV1b0z/FEbafOsfo2bs5m5bLkzfV4dGutXB30SwoEREREREREREREVvR+L/YkoujA7iYEydOEB8fT/fu3Ytf8/Pzo127dmzcuJHBgwezceNG/P39iwtqAN27d8fJyYnNmzdzxx13/OO58/Pzyc/PL/48IyPDfjcichGtqldm+bNdyTdbqORWZlNRRERERERERERERERwcPvHS4mPjwcgJCTkgtdDQkKKt8XHxxMcHHzBdhcXFwICAor3+SdvvfUWfn5+xR8RERE2jl7k8jg7mVRQExEREREREREREREpB8psUc2exo4dS3p6evHH6dOnHR2SiIiIiIiIiIiIiIiIlGFltqgWGhoKQEJCwgWvJyQkFG8LDQ0lMTHxgu1ms5nU1NTiff6Ju7s7vr6+F3yIiIiIiIiIiIiIiIiIXEyZLarVrFmT0NBQ/vvf/xa/lpGRwebNm+nQoQMAHTp0IC0tje3btxfvs3LlSqxWK+3atSv1mEVERERERERERERERKRicuhiTllZWRw9erT48xMnTrBr1y4CAgKIjIxk1KhRvPHGG9StW5eaNWsybtw4wsLC6N+/PwANGjTglltuYfjw4UyePJnCwkKefPJJBg8eTFhYmIPuSkRERERERERERERERCoahxbVtm3bxk033VT8+bPPPgvAAw88wLRp03jhhRfIzs7m0UcfJS0tjc6dO7N06VI8PDyKj5kxYwZPPvkkN998M05OTgwcOJBPPvmk1O9FREREREREREREREREKi6TYRiGo4NwtIyMDPz8/EhPT9f6aiIiIiIiIiIiIiIiFYTG/8WWyuyaaiIiIiIiIiIiIiIiIiJlhYpqIiIiIiIiIiIiIiIiIiVw6JpqZcX5DpgZGRkOjkRERERERERERERERGzl/Li/VsISW1BRDcjMzAQgIiLCwZGIiIiIiIiIiIiIiIitZWZm4ufn5+gwpJwzGSrPYrVaiY2NxcfHB5PJ5OhwRP4mIyODiIgITp8+rcU0RWxM+SViP8ovEftRfonYl3JMxH6UXyL2o/z6Z4ZhkJmZSVhYGE5OWhFLro1mqgFOTk6Eh4c7OgyREvn6+uoNUcROlF8i9qP8ErEf5ZeIfSnHROxH+SViP8qvv9MMNbEVlWVFRERERERERERERERESqCimoiIiIiIiIiIiIiIiEgJVFQTKQfc3d155ZVXcHd3d3QoIhWO8kvEfpRfIvaj/BKxL+WYiP0ov0TsR/klYn8mwzAMRwchIiIiIiIiIiIiIiIiUpZpppqIiIiIiIiIiIiIiIhICVRUExERERERERERERERESmBimoiIiIiIiIiIiIiIiIiJVBRTURERERERERERERERKQEKqqJiIiIiIiIiIiIiIiIlEBFNRERERFxKLPZDIBhGA6ORERERERERETk4lwcHYCIXLuCggK+/fZbqlSpQuvWralVq5ajQxKpMAoLC5k/fz5BQUE0bNiQkJAQR4ckUiEYhoFhGDz66KOkpaUxZ84cTCaTo8MSqXDi4+NxcXEhMDAQwzCUZyI29te8Uo6J2I/yS0REygqToUeCRcq1efPm8dBDD1GvXj1iY2Px9vZmzJgxPPjgg44OTaTcmzRpEi+99BINGjTg4MGDtGjRgrFjx9K9e3f9UidiA6dPn6ZBgwbk5OSwcOFC+vbti9VqxclJzRRErlVhYSEjR45k/fr1jBo1iuHDhzs6JJEKpaCggDfeeIO8vDwiIiJ46qmnHB2SSIVRUFDAJ598gq+vL82bN6dt27aODkmkwigsLGT69On4+/vToEEDoqKiHB2SSLmjEQuRcswwDL766iseffRRtmzZwrJlyxg8eDCPPfYYq1evdnR4IuVWfn4+//nPf/jiiy/44osv+OOPP5g7dy5BQUF8+eWXKqiJ2MiWLVvo3r07Tz/9NCNHjgRQQU3EBqKjo2nVqhX79+9n0qRJ9OzZs3h2qIhcu59//pnw8HDWrl3LmTNnGDVqFGPGjAHUyljkWi1evJiqVasyZ84cPvnkE/r27ctbb73l6LBEKoQvv/ySkJAQpkyZwqhRoxgwYAA///wzAFar1cHRiZQfGrUQKWf++kva3r172bBhAwMHDgSgYcOGvPrqq3Tv3p0XXniBs2fPOipMkXLpfH6lp6dz7tw5Hn30UQYPHoyrqys33XQTNWvWJDMzk8LCQgdHKlL+nM8vwzCKf2Fzd3fH2dmZESNGkJWVxRtvvOHIEEUqjEWLFlGjRg3Wr1/PDTfcgKenJyaTSQ+EiNjAuXPnmDRpEs888wyrVq1ixowZfPTRR8yYMUMPXonYwNSpU7n77rvZtGkTy5cv56233uKll15i2rRpFBQUODo8kXLJbDbz0Ucf8fnnn/PZZ5+xdu1aFi1aRPfu3XnnnXfULUTkCilbRMqR8ePHM23atOLPw8PDMZlMxMbGAhT/gDl58mS2b9/O0qVLHRGmSLn01/wKCgpi6NChxW18zhcAQkNDycnJwc3NzVFhipRLf80vk8lU/Avbxo0bCQ8Pp379+jz//PO8/fbbJCcnM3v2bOLi4hwYsUj5cr5obbFYyM3NZf369fTs2ZPk5GTuuusuevXqRZcuXXj55Zcxm80Ojlak/Pnrg407d+5k3bp1DBo0CCh6Xzt37hyPPPLIBWuricjlsVgsxX8/fvw4mzZtomvXrgBUrVqVhx56iAceeIBPP/2UnTt3OipMkXLpfH7l5+eTlZXFnXfeyeDBgwFo2rQpjRo1wtnZmaSkJEeGKVLuqKgmUg5MnjwZHx8fFi1aRKdOnYpft1gs9OrVi+nTpwPg5uaG2WwmMjKShx9+mE8//dRRIYuUG/+UXyaTiWbNmgFc8MTWkiVLivv5a1BSpGQXe/86X6g2mUxERkYCMGrUKCpXrkxwcDDTpk1Tjolcpr8WrZ2dnfH09OTAgQOcO3eODz/8EGdnZ1555RV69uzJO++8w5tvvklGRoZjgxYpR/7/g41NmzbFx8eHDz74gG3btvHCCy8wYcIE5s+fT8OGDVmwYAF5eXmOC1ikHHn55Zd56aWXij+vWbMmBQUFnDt3DoDc3FwA3n33XeLi4li8eLFmq4lcpr/ml5eXF0OGDGH8+PE4OTkVP/zh7+9PdnY2wcHBjgxVpNxRUU2kDDt69Cjt2rVjzJgxfP311+zcuZN69eoVbw8KCqJdu3acOnWKWbNmXXDs3XffTVxcHIcPHy7tsEXKhZLyC4qeMj7/A2d6ejrHjx/npptuAsDFxaV4P/UeF7lQSfl1vlB97NgxatSoQXR0NG3atCke6B87diwRERF60l/kEv6paH3+/ej222/nvffeY+HChbzyyiv079+fcePG8Z///IcpU6YQExPjyNBFyoWLPRgSGBjI119/TUpKCkOHDuWXX35hyZIlfPXVV/Ts2ZPHH3+cbdu2OTBykbJv4cKFhIaGsmzZMsLCwkhNTQWK3scGDBjA5MmTAfD09KSwsJDAwECGDx/OtGnTcHZ2dmToImXe/8+vlJQUoKhoDUV5dn5m9dKlS2nRogUmk0nLXIhcARXVRMqwrVu3cvToUV5//XUGDx5MWloaP/30E5s2beL48eNA0aBJzZo1mTx5MikpKcUD/UeOHMHb2xs/Pz9H3oJImXWp/Dp16tQF+5pMJuLi4rBYLLRq1QqAPXv28MILLwCo97jI/3M5+ZWVlUVhYSFPPPEETZo04aabbmLLli3069ePZ599FkDr0oj8g0sVrc+/H3Xo0IHg4GA8PT2pV69ecbFt9OjRJCcn66ErkUu4nAevBg4cyKRJkwgKCuKjjz6iR48etGvXjo8++giLxcLu3bsdFL1I2Zednc23337LyJEj2bJlC08//TQBAQFA0Yzr7t27k5+fz8cffwz874GRYcOGkZycrBaQIpfwT/lVpUqVC/ZxcnLCYrFgNpvZvXs3Xbp0AcDV1bV4Hz04LHJpLiXvIiKOcs899/Drr7+yfPly9u/fz5IlS6hWrRpHjx7F19eXn376idatW/PII4/w4osvcs899zBhwgTCwsJYvnw57du3JzAw0NG3IVImlZRfP//8c3EBDYpaPzZo0AAfHx8efvhhvvvuO+67777imTQa/Bf5n8t9/2rQoAHu7u4899xztGzZEoBnnnmGm266iZ07d9KiRQsH34lI2fNPRevff/+d6tWrExISQs2aNenYsSO9evUqXme3TZs2AERHR1O1alW8vb0dfBciZdelcqxq1apUr14dgKSkJA4cOECHDh2Aotb8cXFxVK5cWevvilzCH3/8waZNm1iwYAHnzp1j4sSJVK5cmfDwcIYMGUL37t1ZvXo1H3zwAXfddRdVq1YFih5qDAwM1HuYyCVcLL8iIyMZPHgwJpMJwzBwdnYmPj6epKSk4jUMd+3axUcffcS0adP04LBICUyG+uqIlAmzZ89m2bJlNG7cmG7dutGkSROg6A3xkUceITg4mNGjR9O2bVuSk5MZN24cR48eZdWqVQQHB7N3716GDBmC2WwmJSWF+vXrM3PmTCIiIhx8ZyKOdzX5dfz4cZYuXUp4eDhms5lbb72V3bt3k5+fT/369fn++++Jiopy8J2JON7V5Nfhw4dZv349fn5+mEymC9r4mM1mMjIyip9YFpG/GzJkCBkZGYSFhV20aH3o0CEeeeQRzp07x7PPPkuXLl2YOHEiR44cYeHChVSuXNnRtyFSZl0qx84/eHXw4EHuuusu2rVrxyuvvIKTkxPjx49n586dzJ07t7jNlogUMQwDk8nElClTWLRoEU8++STDhw+nXr16uLu789tvvzFixAgmTJhAeno6Q4YMIS8vjxdeeIHWrVvz0ksvkZGRwbx58/Dw8HD07YiUKZeTX0888QT//ve/CQsLA2D69Ol8/fXX/Prrr4waNYoffviB2267jXnz5mEymfTgsMglqKgm4mApKSk8/vjjrFu3jv79+7Nv3z5OnDjBG2+8wbBhw4Cifv5169bl5ptvLj7u3LlzhIaG8sMPP3DXXXcBkJGRQWJiIunp6RfMsBG5Xl1rfk2fPp1BgwaRnp5Ov379OHv2LB999BF9+vRx0B2JlB22yi+r1aonIUUu4mofulqxYgVhYWEkJSUxfPhwjh07RmZmJtWrV2fatGka7Bf509Xk2LFjx1i5ciWVK1dmzpw5DB8+nNq1a5OcnExUVBTffPMNtWrVcvCdiTjexfJr1qxZPP744wwZMoTAwEBefvllXF1d+e6775g0aRKDBg3iueeeIyEhgWHDhhETE0NaWho1atRgxowZ1KhRw7E3JlIGXE1+TZ48mTvuuIMXXngBwzC45557mDNnDpUqVaJ+/fr88MMPenBY5HIZIuJQs2fPNtq2bWucOXOm+LWBAwcatWvXNubMmWMYhmHk5OT87bj09HSjRo0axrhx44pfs1qt9g9YpBy51vx6+eWXi1/btm2b/QMWKUds+f4lIhdKTk427rzzTiM0NNR4/PHHjc6dOxvVqlUzpk6dWrzPpEmTjBUrVlxwXGpqquHm5mb8/PPPxa/l5+cbycnJxv79+0srfJEy71pzbNasWcWvHThwwFi+fLmxfv360gpfpEwrKb+sVqvRuHFjw2QyXZBzVqvVGDhwoPHwww8b+fn5hmEYRl5enhEXF2fs2bPHAXciUvZca3498sgjRmFhoWG1Wo3BgwcbNWrUMH777TfH3IxIOabHgkUcbObMmYSHh1OtWjWysrIA6NevH8ePH+eLL74gKSkJT0/P4nWbztu4cSOenp7Fs9RAazqJ/H/Xml9333138Wua/SlyIVu+f4nIhVatWkVMTAzbtm1j0qRJrF27lvbt2/PGG28wd+5cAB544IELZoECODs7ExYWxt69e4tfc3Nzo0qVKjRs2LBU70GkLLvWHNu3b1/xaw0aNKB79+507NixVO9BpKy6WH69/vrrLFiwAJPJxBNPPAFATk4OZrMZKBrP8PHx4cCBA8XrErq5uREaGlo8C0fkenet+bVv3z5cXFwwmUxMmDCBEydOcNtttznylkTKJRXVRErRmjVr+P3334vf1ADq1q3L/v37AYoX3D148CDdunUjLy+PBQsWAEVvgHFxcRw9epQvv/ySRx99lB49elC7du2/DViKXI+UXyL2o/wSKV0qWovYly0fvBKRC10sv06cOMGnn35KSkoKjz32GD169ODTTz9l5cqVAMTHxxMbG8tDDz1UfC49OCxyoWvNr4cffrj4XHXr1nXIPYhUBCqqiZSC5ORkHnjgAW688UbGjBnDmTNnirc99thjpKam0rVrV9599106duzITz/9xIsvvkh2djanT58GIC8vj+XLl9O7d28mTpzIm2++yccff4ynp6d+0JTrmvJLxH6UXyL2p6K1iH0px0Ts50rzKzc3l3nz5uHk5MSMGTMIDg7m3nvv5bbbbqN58+YUFhbSu3dvh9yLSFmj/BIpu1RUE7Ezs9nM7NmzSUhIYNasWRw5coRZs2aRn58PQK1atZg/fz716tVj1qxZtGrVii1bttCtWzeaNm3KgQMHAPDw8ODWW2/l3Xff5fjx4wwZMsSRtyVSJii/ROxH+SViXypai9iXckzEfq42v3Jycjh9+jQWi4XAwEBmz57Njz/+SLdu3Zg8eTIrV66katWqDrwzEcdTfomUfS6ODkCkonNxcaFly5aEh4fTt29fDh06xAcffMAtt9xC8+bNAejUqROdOnWioKCguHd4YmIiO3fuZPDgwQBYrVaCgoLo16+fo25FpMxRfonYj/JLxH7+f9H6wQcfZNasWTzzzDO4u7sXF62nTZvGrFmz6NixI6+88gqBgYH/WLT29/dXjon8hXJMxH5skV/Ozs4ABAcH06NHD3r06OHguxIpG5RfIuWDyVDPAhG7MwzjgicZq1WrRp8+fXjvvffw8fG5YHteXh7Ozs588803fP3113z33XdalFfkEpRfIvaj/BKxn82bN5OYmEjfvn2ZMGECn332GcuWLSsuWp/3/4vWN998M4MHD+all17CarXi5KTmIyL/RDkmYj/KLxH7UX6JlH3KLpFScH7AsaCgAICPPvqIKVOmsGnTpgu2nz17lqlTp9KxY0defvllRo8erQFJkRIov0TsR/klYj9t27alb9++AIwfPx5XV1cmTZpEZmYmQPF6TW5ubuTl5VFYWMjcuXNxdXUtnjGjwRKRi1OOidiP8kvEfpRfImWfZqqJOEjHjh3x8vIqXjw0KSmJoKAgfvzxR2JjY3nuueccHaJIuaX8ErEf5ZeIbZ1/ynj27Nnce++9LF68+II2PWfPnuWXX35hypQpHD9+nM8++4x77rnHgRGLlC/KMRH7UX6J2I/yS6TsUlFNpJSZzWZcXFzYv38/zZo144MPPuDYsWOsW7eO7777jsaNGzs6RJFyS/klYj/KLxH7U9FaxL6UYyL2o/wSsR/ll0jZoqKaiAO1bduWbdu2ERkZyZdffkmvXr0cHZJIhaH8ErEf5ZeIbaloLWJfyjER+1F+idiP8kukbFJRTcQBjh07Rv/+/Tl+/DiffPIJDz/8sKNDEqkwlF8i9qP8ErE/Fa1F7Es5JmI/yi8R+1F+iZQdWrVQxAGcnZ0ZOHAgycnJGpAUsTHll4j9KL9E7OfYsWM0adKE/fv38/XXX3Py5EkNlojYkHJMxH6UXyL2o/wSKXs0U01ERERERMTBTp48ybRp0xgzZgyenp6ODkekwlGOidiP8kvEfpRfImWPimoiIiIiIiIiIiIiIiIiJVD7RxEREREREREREREREZESqKgmIiIiIiIiIiIiIiIiUgIV1URERERERERERERERERKoKKaiIiIiIiIiIiIiIiISAlUVBMREREREREREREREREpgYpqIiIiIiIiIiIiIiIiIiVQUU1ERERERERERERERESkBCqqiYiIiIhImTNs2DD69+9f6tedNm0aJpMJk8nEqFGjLrlvjRo1+Oijjy7rvDfeeGPxeXft2nXNcYqIiIiIiEjpc3F0ACIiIiIicn0xmUyX3P7KK6/w8ccfYxhGKUV0IV9fX6Kjo/Hy8rLZOefNm8exY8do27atzc4pIiIiIiIipUtFNRERERERKVVxcXHFf//pp58YP3480dHRxa95e3vj7e3tiNCAoqJfaGioTc8ZEBBARkaGTc8pIiIiIiIipUvtH0VEREREpFSFhoYWf/j5+RUXsc5/eHt7/63944033shTTz3FqFGjqFy5MiEhIXz99ddkZ2fz4IMP4uPjQ506dViyZMkF19q3bx+33nor3t7ehISEMHToUJKTk6845sTERPr27Yunpyc1a9ZkxowZF2w3DINXX32VyMhI3N3dCQsL4+mnn76qr4+IiIiIiIiUTSqqiYiIiIhIufDdd98RGBjIli1beOqppxgxYgSDBg2iY8eO7Nixg549ezJ06FBycnIASEtLo1u3brRo0YJt27axdOlSEhISuOuuu6742sOGDeP06dOsWrWKOXPm8MUXX5CYmFi8fe7cuXz44Yd8+eWXHDlyhAULFtCkSROb3buIiIiIiIg4nto/ioiIiIhIudCsWTNefvllAMaOHcvbb79NYGAgw4cPB2D8+PFMmjSJPXv20L59ez777DNatGjBm2++WXyOKVOmEBERweHDh6lXr95lXffw4cMsWbKELVu20KZNGwC+/fZbGjRoULxPTEwMoaGhdO/eHVdXVyIjI7V+moiIiIiISAWjmWoiIiIiIlIuNG3atPjvzs7OVKlS5YLZYCEhIQDFM8h2797NqlWritdo8/b2JioqCoBjx45d9nUPHjyIi4sLrVq1Kn4tKioKf3//4s8HDRpEbm4utWrVYvjw4cyfPx+z2XxV9ykiIiIiIiJlk2aqiYiIiIhIueDq6nrB5yaT6YLXTCYTAFarFYCsrCz69u3LxIkT/3auqlWr2jS2iIgIoqOjWbFiBcuXL+eJJ57g3XffZfXq1X+LW0RERERERMonFdVERERERKRCatmyJXPnzqVGjRq4uFz9rz5RUVGYzWa2b99e3P4xOjqatLS0C/bz9PSkb9++9O3bl5EjRxIVFcXevXtp2bLltdyGiIiIiIiIlBFq/ygiIiIiIhXSyJEjSU1N5Z577mHr1q0cO3aM33//nQcffBCLxXLZ56lfvz633HILjz32GJs3b2b79u088sgjeHp6Fu8zbdo0vv32W/bt28fx48eZPn06np6eVK9e3R63JiIiIiIiIg6gopqIiIiIiFRIYWFhrF+/HovFQs+ePWnSpAmjRo3C398fJ6cr+1Vo6tSphIWF0bVrVwYMGMCjjz5KcHBw8XZ/f3++/vprOnXqRNOmTVmxYgWLFi2iSpUqtr4tERERERERcRCTYRiGo4MQEREREREpC6ZNm8aoUaP+1trRFk6ePEnNmjXZuXMnzZs3t/n5RURERERExL40U01EREREROQv0tPT8fb2ZsyYMTY756233kqjRo1sdj4REREREREpfZqpJiIiIiIi8qfMzEwSEhKAopaOgYGBNjnv2bNnyc3NBSAyMhI3NzebnFdERERERERKj4pqIiIiIiIiIiIiIiIiIiVQ+0cRERERERERERERERGREqioJiIiIiIiIiIiIiIiIlICFdVERERERERERERERERESqCimoiIiIiIiIiIiIiIiEgJVFQTERERERERERERERERKYGKaiIiIiIiIiIiIiIiIiIlUFFNREREREREREREREREpAQqqomIiIiIiIiIiIiIiIiU4P8AS5iz53fqYoMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1600x350 with 1 Axes>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_series(Y_df, Y_hat_df2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "https://pytorch-lightning.readthedocs.io/en/stable/common/checkpointing_basic.html\n",
    "\n",
    "[Oreshkin, B. N., Carpov, D., Chapados, N., & Bengio, Y. (2019). N-BEATS: Neural basis expansion analysis for interpretable time series forecasting. ICLR 2020](https://arxiv.org/abs/1905.10437)\n",
    "\n",
    "[Cristian Challu, Kin G. Olivares, Boris N. Oreshkin, Federico Garza, Max Mergenthaler-Canseco, Artur Dubrawski (2021). N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting. Accepted at AAAI 2023.](https://arxiv.org/abs/2201.12886)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
